FACULTY OF MATHEMATICS AND NATURAL SCIENCE
FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM FACULTY OF MATHEMATICS AND NATURAL SCIENCE
Program Studi STATISTIKA Department
Statistics
Jenjang Pendidikan Magister Sains Programme
[Master of Science]
x [diisi maksimum 5 kompetensi lulusan yang utama dan ditulis dengan bullet]
Kompetensi
Mampu melakukan penelitian pengembangan
Lulusan
dan penerapan statistika yang berkontribusi pada penyelesaian masalah riil di masyarakat
Graduate
x [ditulis terjemahan bahasa inggris dengan cetak miring]
Competence
x Able to do development research in Statistics methods and to applly Statistics that have contribution to solve on real problem
STRUKTUR KURIKULUM/COURSE STRUCTURE
No. Kode MK
Sks Code
Nama Mata Kuliah (MK)
Credits SEMESTER I
Course Title
1 SS09 2301
Teori Probabilitas
3 Probability Theory
2 Mata Kuliah Pilihan 1
3 Optional Subjects/Course 1
3 Mata Kuliah Pilihan 2
3 Optional Subjects/Course 2
3 ITS : 2009-2014
4 Mata Kuliah Pilihan 3
Optional Subjects/Course 3
12 rriculum MATA KULIAH PILIHAN SEMESTER I/ Optional Subjects/Course Semester I
Jumlah sks/Total of credits
Cu
1 SS09 21 Analisis Statistika 3
Statistical Analysis
2 Desain Eksperimen 3
SS09 2212
Design of Experiment Kurikulum/
3 3 SS09 2213
Model Linear Linear Models
4 SS09 2221
Riset Operasi 3 Operation Research
5 SS09 2222
PPIC 3
Product Planing an Inventory Control 6
Stat. Pros. Control 3 Statistical Process control
SS09 2223
7 3 SS09 2231
Teknik Simulasi Simulation Technique
8 SS09 2232
Metode Resampling 3 Resampling methods
9 Studi Kependudukan 3
SS09 2241
Demographic Study
3 SS09 2242
10 Riset Pemasaran
Marketing Research
11 SS09 2243
Statistik Ofisial 3 Official Statistics
SEMESTER II
1 SS09 2302 Statistik Inferensi 3 Inference Statistics
2 Mata Kuliah Pilihan 4
3 Optional Subjects/Course 4
3 Mata Kuliah Pilihan 5
3 Optional Subjects/Course 5
4 Mata Kuliah Pilihan 6
3 Optional Subjects/Course 6
Jumlah sks/Total of credits
MATA KULIAH PILIHAN SEMESTER II/ Optional Subjects/Course Semester II
1. SS09 2214
Analisis Multivariat 3 Multivariate Analysis
2. SS09 2215
An. Data Kualitatif 3 Qualitative data Analysis ITS : 2009-2014
3. SS09 2216
Statistik Spasial
Spatial Statistics
4. SS09 2217
Reg. Nonparametrik 3 rriculum Nonparametric regression Cu
5. SS09 2218
An. Deret Waktu
Time Series Analysis
6. SS09 2219
Proses Stokastik 3 Stocastic Process Kurikulum/
7. SS09 2223
Stat. Pros. Control 3 Statistical Process control
8. SS09 2224
Teori Antrian 3 Queueing Theory
9. SS09 2225
Peranc. Kualitas 3 Quality design
10. SS09 2226
Analisis Realibilitas 3 Reliability Analysis
11. SS09 23
Analisis Bayesian 3 Bayesian Analysis
12. SS09 2234
Neural Network 3 Neural Network
13. SS09 2235
Data Mining 3 Data Mining
14. SS09 2243
Statistik Ofisial 3 Official Statistics
15. SS09 24
Ekonometrika 3 Econometrics
16. SS09 2245
Aktuaria 3 Actuaria
ITS : 2009-2014
rriculum Cu
SEMESTER III
1 SS09 2302 Analisis Data 3 Data Analysis
2 Mata Kuliah Pilihan 7
3 Optional Subjects/Course 7
Jumlah sks/Total of credits
MATA KULIAH PILIHAN SEMESTER III/ Optional Subjects/Course Semester III
1. SS09 2216 Statistik Spasial 3 Spatial Statistics
2. SS09 2217 Reg. Nonparametrik 3 Nonparametric regression
3. SS09 2218 An. Deret Waktu 3 Time Series Analysis
4. SS09 2219 Proses Stokastik 3 Stocastic Process
5. SS09 2225 Peranc. Kualitas 3 Quality design
6. SS09 2226 Analisis Realibilitas 3 Reliability Analysis
7. SS09 23 Analisis Bayesian 3 Analisis Bayesian
8. SS09 2234 Neural Network 3 Neural Network
9. SS09 2235 Data Mining 3 Data Mining
10. SS09 2236 Stat. Komp. Intensif 3 Intensive Computational statistics
11. SS09 24 Ekonometrika 3 Econometrics
12. SS09 2245 Aktuaria 3 Actuaria
ITS : 2009-2014
rriculum Cu
SILABUS KURIKULUM/COURSE SYLLABUS
SS09 2301: Teori Probabilitas
MATA KULIAH/
SS09 2301: Probability Theory
COURSE TITLE
Credits: tiga/three Semester: I
Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel
TUJUAN
random, model-model probabilitas, hukum bilangan besar, teorema PEMBELAJARAN/ limit pusat dan fungsi variabel random
LEARNING
[Understanding concept of random experiment, random variable,
OBJECTIVES
probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable]
x Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel
KOMPETENSI/
random, model-model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random
COMPETENCY
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable]
x Variabel random, ruang probabilitas, fungsi distribusi, ekspektasi dan momen, konvergensi variabel random, fungsi karakteristik, distribusi bersyarat dan kebebasan stokastik, hukum bilangan besar, distribusi khusus, distribusi fungsi variabel random,
POKOK
distribusi limit. Pengantar teori peluang. Transformasi variabel random dan statistik berurut. Fungsi pembangkit momen
BAHASAN/
[Random variable, probability space, distribution function,
SUBJECTS
x expectation and moment, convergence of random variables,
characteristic function, conditional distribution and stochastic independence, Law of Large Numbers, special distribution,
ITS : 2009-2014 distribution of random variable function, limit distribution.
Introduction to probability theory. Transformation of random variables and order statistics. Moment generating function]
rriculum
1. Bartoszynski, R., 1996, Probability and Statistical Inference, John Cu
PUSTAKA
Wiley & Sons, New York.
UTAMA/
2. Bhat, B.R., 1981, Modern Probability Theory, John Wiley & Sons,
REFERENCES
New York.
3. Hogg, R.V. and Tanis, E.A., 1993, Probability and Statistical
SS09 2302: Statistik Inferensi
MATA KULIAH/
SS09 2302: Inference Statistics
COURSE TITLE
Credits: tiga/three
Semester: II
Mampu memahami konsep penaksiran, metode penentuan penaksir, sifat-sifat penaksir, fungsi kerugian dan resiko, statistik kecukupan.
TUJUAN
Keluarga eksponensial, ketidakbiasan, equivariance, uniformly most PEMBELAJARAN/ powerfull test, ketidakbiasan untuk uji hipotesis, hipotesis linier
LEARNING
[Able to understand concept of estimation, methods of finding
OBJECTIVES
estimators, properties of estimators, loss and risk function, sufficiency. Exponential family, Unbiasedness, equivariance, uniformly most powerfull test, unbiasedness for hypothesis test, linier hypothesis]
x Mampu memahami konsep penaksiran, metode penentuan penaksir, sifat-sifat penaksir, fungsi kerugian dan resiko, statistik kecukupan. Keluarga eksponensial, ketidakbiasan, equivariance,
KOMPETENSI/
uniformly most powerfull test, ketidakbiasan untuk uji hipotesis, hipotesis linier
COMPETENCY
x [Able to understand concept of estimation, methods of finding estimators, properties of estimators, loss and risk function, sufficiency. Exponential family, Unbiasedness, equivariance, uniformly most powerfull test, unbiasedness for hypothesis test, linier hypothesis]
x Penaksiran, meliputi penaksiran titik, penaksiran interval. Statistik kecukupan, ketakbiasan, penaksir efisien, penguji
POKOK
hipotesis. UMPT. Uji hipotesis pada sampling distribusi normal.
BAHASAN/
Uji Chi-square, hipotesis linear, dan hipotesis multivariate linier
SUBJECTS
x Estimation, covers point estimation, interval estimation. Sufficiency, unbiasedness, efficient estimators, hypothesis testing,
ITS : 2009-2014 UMPT, hypothesis testing of sampling normal distribution, Chi- square test, linier hypothesis, and linier multivariate hypothesis
1. Bartoszynski, R., 1996, Probability and Statistical Inference, John rriculum Wiley & Sons, New York.
PUSTAKA
Cu
2. Hogg, R.V. and Tanis, E.A., 1993, Probability and Statistical Inference; Macmillan Publishing Co., New York.
UTAMA/
REFERENCES
3. Lehman, E.L. 1983, Theory of Point Estimation, John Wiley & Sons: New York.
Sons: New York.
SS09 2303: Analisis Data
MATA KULIAH/
SS09 2303: Data Analysis
COURSE TITLE
Credits: tiga/three
Semester: III
Mampu memahami penggunaan paket program Statistik, khususnya MINITAB, SPSS, SAS, dan R, untuk menyelesaikan permasalahan real, yaitu problem tentang pemodelan regresi, analisis multivariat, analisis data kualitatif, regresi nonparametrik, analisis time series, dan metode
TUJUAN
resampling. Mampu membuat suatu laporan ilmiah hasil analisis suatu PEMBELAJARAN/ permasalahan real
LEARNING
[Able to understand usage of statistical program packages, especially
OBJECTIVES
MINITAB, SPSS, SAS, and R, for solving real problem, which are problems on regression model, multivariate analysis, qualitative data analysis, nonparametric regression, time series analysis, and resampling method. Able to produce a scientific report based on a real problemsalahan real]
x Mampu memahami penggunaan paket program Statistik, khususnya MINITAB, SPSS, SAS, dan R, untuk menyelesaikan
permasalahan real, yaitu problem tentang pemodelan regresi, analisis multivariat, analisis data kualitatif, regresi nonparametrik, analisis time series, dan metode resampling. Mampu membuat
KOMPETENSI/
suatu laporan ilmiah hasil analisis suatu permasalahan real
COMPETENCY
x [Able to understand usage of statistical program packages, especially MINITAB, SPSS, SAS, and R, for solving real problem, which are problems on regression model, multivariate analysis,
qualitative data analysis, nonparametric regression, time series analysis, and resampling method. Able to produce a scientific report based on a real problemsalahan real]
ITS : 2009-2014 x Bahasa pemrograman paket program statistika, yang meliputi
telaah terhadap program-program komputer (khususnya MINITAB,
POKOK
SPSS, SAS, dan R) dan penerapan model-model statistika. Studi rriculum
BAHASAN/
kasus real dengan penerapan beberapa metode statistik lanjut, Cu yaitu analisis mulivariate, analisis data kualitatif, Generalized
SUBJECTS
Linear Models, regresi nonparametrik, regresi nonlinear (uji nonlinearitas), analisis deret waktu, nonlinear time series, dan resampling methods Linear Models, regresi nonparametrik, regresi nonlinear (uji nonlinearitas), analisis deret waktu, nonlinear time series, dan resampling methods
1. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (2006) “Multivariate Data Analysis”, 6th edition, Prentice Hall
International: UK.
2. Sharma, S. (1996). “Applied Multivariate Techniques”, New-York: John Wiley & Sons, Inc.
3. Johnson, N. and Wichern, D. (1998). “Applied Multivariate Statistical Analysis”, Prentice-Hall, Englewood Cliffs, N.J
4. McCullagh P. and Nelder, J.A. (1989) Generalized Linear Models. London: Chapman and Hall.
5. Hosmer, D.W. and Lemeshow, S. (2000). Applied Logistic Regression. 2nd Edition, New-York: John Wiley & Sons.
6. Wand, M. P. and Joes, M. C. (1995). Kernel Smoothing. Chapman and Hall, London .
PUSTAKA
7. Heckman, N. and Ramsay, J. O. (1996). Spline smoothing with
UTAMA/
model based penalties. McGill University, unpublished manuscript.
REFERENCES
8. Shumway, R.H. and Stoffer, D.S. (2006). Time Series Analysis and Its Applications with R Examples. Second edition, Springer: New
York, USA.
9. Wei, W.W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods. Second edition, Addison-Wesley Publishing
Co., USA.
10. Box, G.E.P, Jenkins, G.E., and Reinsel, H. (1994). Time Series Analysis.
11. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
12. Tong, H. (1994). Nonlinear Time Series. John Wiley & Sons.
13. Manual SAS, SPSS, MINITAB, dan R. ITS : 2009-2014
14. Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer rriculum
SS09 2304: Tesis Cu
MATA KULIAH/
SS09 2304: Thesis
COURSE TITLE
Credits: enam/six
Semester: IV
Mampu menyelesaikan persoalan Statistika Industri, Bisnis-Sosial, atau Komputasi, dengan metode statistika terbaru dan membandingkan
TUJUAN
dengan metode sebelumnya, serta dapat mempublikasikan hasil PEMBELAJARAN/ kajiannya dalam suatu forum ilmiah nasional atau internasional
LEARNING
(seminar dan/atau jurnal)
OBJECTIVES
[Able to solve statistical problems on industry, social-business, or computation, using the newest statistical method and compare it with the previous method, and also can publish the study result in a national or international scientific forum (seminar and/or journal)
x Mampu menyelesaikan persoalan Statistika Industri, Bisnis-Sosial, atau Komputasi, dengan metode statistika terbaru dan membandingkan dengan metode sebelumnya, serta dapat mempublikasikan hasil kajiannya dalam suatu forum ilmiah
KOMPETENSI/
nasional atau internasional (seminar dan/atau jurnal)
COMPETENCY
x [Able to solve statistical problems on industry, social-business, or computation, using the newest statistical method and compare it with the previous method, and also can publish the study result in
a national or international scientific forum (seminar and/or journal)] x Kegiatan penelitian mandiri dimulai dari pembuatan proposal
penelitian, seminar proposal dan pelaksanaan penelitian. Hasil penelitian harus diseminarkan dan dipertanggungjawabkan dihadapan penguji dalam ujian tesis, serta dipublikasikan dalam
POKOK
suatu forum ilmiah nasional atau internasional (seminar dan/atau
x [Independent research activities starting from producing a
research proposal, proposal seminar, and research implementation. Result of the research should be presented in a seminar and can be accountabled in front of examiners during thesis examination, also should be published in a national or international scientific forum (seminar and/or journal)]
PUSTAKA
Manual of how to write proposal, thesis and dissertation report based
UTAMA/
on quality standard of PPS-ITS ITS : 2009-2014
REFERENCES
rriculum Cu
SS09 2211: Analisis Statistika
MATA KULIAH/
SS09 2211: Statistical Analysis
COURSE TITLE
Credits: tiga/three
Mampu memahami teori dan metode statistika dasar. Mampu
TUJUAN
menganalisis hasil metode statisika dasar, dan memberikan
PEMBELAJARAN/ interpretasi hasil suatu analisis data dengan metode statistika dasar
LEARNING
[Able to understand theory and method of basic statistics. Able to
OBJECTIVES
analyse result of basic statistical method and give interpretation of the result of data analysis using basic statistical method]
x Mampu memahami teori dan metode statistika dasar. Mampu menganalisis hasil metode statisika dasar, dan memberikan
KOMPETENSI/
interpretasi hasil suatu analisis data dengan metode statistika
COMPETENCY
dasar x [Able to understand theory and method of basic statistics. Able to
analyse result of basic statistical method and give interpretation of the result of data analysis using basic statistical method]
x Pengantar Probabilitas. Estimasi parameter, meliputi estimasi titik dan interval. Uji hipotesis tentang rata-rata, proporsi, dan varians
POKOK
pada satu dan dua populasi. Analisis korelasi, regresi sederhana
BAHASAN/
dan berganda. Uji independensi dan analisis nonparametrik dasar
x Introduction to probability. Parameter estimation, covers point and interval estimation. Hypothesis testing of mean, proportion, and
SUBJECTS
varians of one and two populations. Correlation analysis, simple regression and multiregression. Independent test and basic nonparametric analysis
PUSTAKA
1. Dowdy, S., Weardon, S., and Chilko, D., 2004, Statistics for
UTAMA/
Research, 3rd Edition, John Wiley & Sons: New York.
REFERENCES
2. Lefebvre, M., 2006, Applied Probability and Statistics, Springer Verlag: New York.
SS09 2212: Desain Eksperimen
MATA KULIAH/
SS09 2212: Design of Experiment
COURSE TITLE
Credits: tiga/three Semester: I ITS : 2009-2014
Memahami berbagai konsep rancangan percobaan, yang meliputi rriculum
TUJUAN
faktorial design, nested design, fraksional faktorial design, split-plot Cu PEMBELAJARAN/ design, confounding, blok tak lengkap, analisis kovariansi, dan metode
[Understanding various concepts of experiment design, which covers factorial design, nested design, fractional factorial design, split-plot
Taguchi method] x Memahami berbagai konsep rancangan percobaan, yang meliputi
faktorial design, nested design, fraksional faktorial design, split- plot design, confounding, blok tak lengkap, analisis kovariansi, dan
KOMPETENSI/
metode Taguchi
COMPETENCY
x [Understanding various concepts of experiment design, which covers factorial design, nested design, fractional factorial design,
split-plot design, confounding, incomplete blocks, covariance analysis, and Taguchi method]
x Konsep dasar perancangan percobaan, justifikasi model linier, pengacakan, pengelompokan dan penggunaan pengamatan
penyerta. Pembahasan mengenai Faktorial design, Nested design,
POKOK
Fraksional faktorial design, rancangan petak terbagi (split-splot
BAHASAN/
design), pembauran (confounding), analisis kovarians, dan metode Taguchi
SUBJECTS
x Basic concept of experiment design, linier model justification, randomization, clustering and penyerta observation usage.
Discussion on factorial design, Nested design, Fractional factorial design, split-splot design, confounding, covarians analysis, and Taguchi method
1. Hinkelmann, K. and Kemptkarne, O., 1994, Design and Analysis of Experiments, John Wiley & Sons, New York.
2. Bagchi, T., 1994, Taguchi Methods Explained Practical Steps to
PUSTAKA
Robust Design, John Wiley & Sons, New York.
UTAMA/
3. Montgomery, D.C., 1997, Design and Analysis of Experiment, John
REFERENCES
Wiley & Sons, New York.
4. Gardiner, W.P. Gettinby, 1998, Experimental Design Techniques in Statistical Practice : A Practical Software-base approach,
Horwood Publishing Limited.
SS09 2213: Model Linier ITS : 2009-2014
MATA KULIAH/
SS09 2213: Linear Model
COURSE TITLE
Credits: tiga/three Semester: I rriculum
Cu Mengerti dan memahami bentuk-bentuk sebaran kuadratik, model
TUJUAN
dasar, penggolongan silang, dwi arah, komponen ragam. Mampu
PEMBELAJARAN/
mengem-bangkan model-model linier untuk regresi, baik dengan rank
LEARNING
OBJECTIVES
[Understanding various distributions of quadratic forms, basic model, cross classification, two way, component style. Able to develop linier models for regression, both by full rank or not of full rank]
x Mengerti dan memahami bentuk-bentuk sebaran kuadratik, model dasar, penggolongan silang, dwi arah, komponen ragam. Mampu
mengem-bangkan model-model linier untuk regresi, baik dengan
KOMPETENSI/
rank penuh ataupun tidak
COMPETENCY
x [Understanding various distributions of quadratic forms, basic model, cross classification, two way, component style. Able to
develop linier models for regression, both by full rank or not of full rank]
x Pendugaan dan pengujian hipotesis beberapa model linear. Model klasifikasi satu-arah dan dwi-arah. Perluasan model-model sel
POKOK
rataan. Model dengan peubah penyerta. Model pengaruh-
BAHASAN/
pengaruh campuran dan pendugaan komponen ragam, serta fungsi estimabel
SUBJECTS
x Estimation and hypothesis test for some linier models. One way and two way classification models. Extension of means cell models. Models with dependent variables. Mixed influence models and variance component estimation and also estimabel functions
1. Bowerman, B.L. and R.T. O’Connel, 1990, Linear Statistical Models an Applied Approach, PWS-KENT Publication Company, Boston.
2. Hocking, R.R., 1996, Methods and Applications of Linear Models Regression and analysis of Variance, John Willey & Sons Inc., New
York.
PUSTAKA
3. Rao, C.R., 1973, Linear Statistical Inference and Its Applications,
UTAMA/
2nd Edition, Eastern Private Limited, New Delhi.
REFERENCES
4. Searle, S.R., 1987, Linear Models for Unbalanced data, John Wiley & Sons Inc., New York.
5. Myers, R.H. and Milton, J.S., 1991, A First Subjects/Course in the Theory of Linear Statistical Models, PWS-KENT Publication
Company, Boston. ITS : 2009-2014
SS09 2214: Analisis Multivariat rriculum
MATA KULIAH/
SS09 2214: Multivariate Analysis Cu
COURSE TITLE
Credits: tiga/three
Semester: II
PEMBELAJARAN/ multivariat, analisis eksplorasi, pereduksi dimensi, pengujian hipotesis
LEARNING
data multivariat, metode multisampel dan analisis diskriminan
OBJECTIVES
[Able to differentiate and interpret univariate data, multivariate data, exploration analysis, reduction dimension techniques, test of hypothesis of multivariate data, multisample method, and discriminant analysis]
x Mampu membedakan dan menginterpretasikan data univariat, data multivariat, analisis eksplorasi, pereduksi dimensi, pengujian
hipotesis data multivariat, metode multisampel dan analisis
x [Able to differentiate and interpret univariate data, multivariate data, exploration analysis, reduction dimension techniques, test of
hypothesis of multivariate data, multisample method, and discriminant analysis]
x Review tentang aljabar linier, dan fungsi distribusi multivariat, yaitu distribusi Multinormal, Wishart, dan T 2 Hotelling. Analisis
eksplorasi yang meliputi Biplot, analisis korespondensi, PCA, analisis faktor, analisis cluster, multidimensional scaling dan analisis konjoin. Analisis konfirmasi, terdiri atas pengujian satu
POKOK
mean dan taksiran interval, serta pengujian dua mean dan
BAHASAN/
taksiran interval. MANOVA, meliputi one-way, two-way, dan faktorial diskriminan linier
SUBJECTS
x Reviewing linier algebra, and function of multivariate distributions which are Multinormal, Wishart, and T 2 Hotelling. Exploration
analysis which covers Biplot, corespondence analysis, PCA, factor analysis, multidimensional scaling and conjoint analysis. Confirmatory analysis, consists of one mean test and interval estimation. MANOVA, consist of one-way, two-way, and linier discriminant factorial
1. Timm, N.H., 2002, Applied Multivariate Analysis, Springer-Verlag: New York.
2. Rencher, A.C., 2002, Method of Multivariate Analysis, John Wiley & Sons : Canada.
ITS : 2009-2014
PUSTAKA
3. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., 2006,
UTAMA/
Multivariate Data Analysis, 6th edition, Prentice Hall
REFERENCES
International: UK. rriculum
4. Sharma, S., 1996, Applied Multivariate Techniques, New-York: John Wiley & Sons, Inc.
Cu
5. Dillon, W.K. and Matthew, G., 1984, Multivariate Analysis, Methods and Application, John Wiley & Sons, New York.
Kurikulum/
SS09 2215: Analisis Data Kualitatif
MATA KULIAH/
SS09 2215: Qualitative Data Analysis
COURSE TITLE
Credits: tiga/three
Semester: II
Memahami inferensi dalam tabel kontingensi 2x2, L2x2, rxk, Lrxk,
rxkxl, model Log linier tabel rxk, rxkxl yang berkategori, model logistik PEMBELAJARAN/ regresi, dan model logistik regresi dengan strata
TUJUAN
LEARNING
[Understanding inference in contingency tables 2x2, L2x2, rxk, Lrxk,
OBJECTIVES
rxkxl, Log linier model, rxk, rxkxl category tables, logistic regression model, and logistic regression model with stratum]
x Memahami inferensi dalam tabel kontingensi 2x2, L2x2, rxk, Lrxk, rxkxl, model Log linier tabel rxk, rxkxl yang berkategori, model
KOMPETENSI/
logistik regresi, dan model logistik regresi dengan strata
COMPETENCY
x [Understanding inference in contingency tables 2x2, L2x2, rxk, Lrxk, rxkxl, Log linier model, rxk, rxkxl category tables, logistic
regression model, and logistic regression model with stratum] x Metode-metode analisis tabel kontingensi berdimensi banyak.
Metode jumlah kuadrat tertimbang, model log-linier dan pendekatan regresi logistik untuk analisis data kategori. Pendugaan parameter dan besaran asosiasi, pemilihan model, dan
POKOK
pengujian kesesuaian model. Penerapan praktis untuk
BAHASAN/
penyelesaian permasalahan real dengan penggunaan paket komputer statistik, khususnya SPSS dan R
SUBJECTS
x Some methods of multidimension contingency tables. Weighted sum square method, log-linier model and logistic regression
approach for categoric data analysis. Parameter estimation and association value, model selection, and fitting model test. Practical application for solving real problem using statistical computer package, especially SPSS and R
1. Agresti, A., 2002, Categorical Data Analysis, 2nd Edition, John ITS : 2009-2014 Wiley & Sons: New York.
PUSTAKA
2. Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W., 2007, Discrete
UTAMA/
Multivariate Analysis: Theory and Practice, Springer: New York. rriculum
REFERENCES
3. Greenacre, M.J., 1984, Theory and Applications of Correspondence Cu Analysis, Academic Proses, Inc., New York.
SS09 2216: Statistik Spasial
MATA KULIAH/
SS09 2216: Spatial Statistics
COURSE TITLE
Credits: tiga/three Semester: III
Memahami konsep dasar data spasial, struktur data spasial, pendugaan dan pemodelan korelasi spasial, prediksi dan interpolasi, mapping pola, regresi spasial dan
TUJUAN
pemodelan spatio-temporal
PEMBELAJARAN/ LEARNING
[ OBJECTIVES Understanding the basical concept of spatial data, spatial
data structure, estimating and modelling spatial correlation, prediction and interpolation, pattern mapping,spatial regression, Spatial-temporal modelling .]
x Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]
Pengertian statistik spasial, Struktur data spasial (titik, area (lattices), dan spasial), isotropic dan stasioner. Pendugaan dan pemodelan korelasi spasial (estimasi
ITS : 2009-2014
POKOK
variogram, MLE, fitting parametric models). Prediksi dan
BAHASAN/
interpolasi (ordinary kriging, cokriging). Mapping pola
SUBJECTS
titik, Regresi spasial (SAR, GWR) dan neighborhood rriculum Cu
analysis. Pemodelan spatio-temporal. [Spatial statistics concepts, Spatial data structure (point,
lattices and spatial), isotropic and stationarity. Estimating lattices and spatial), isotropic and stationarity. Estimating
1. Cressie, N., 1993, Statistics for Spatial Data, John Wiley & Sons.
2. Wackernagel, H., 1995, Multivariate Geostatistics, An
PUSTAKA
Introduction with Applications, Springer-Verlag.
UTAMA/
3. Sandra L.A., 1996, Practical handbook of Spatial
REFERENCES
Statistics. CRC Press. Inc. USA. Isaaks, E.H. and Srivastava, R.H., 1989, Applied Geostatistics, Oxford University Press.
4. Isaaks, E.H. and Srivastava, R.H., 1989, Applied Geostatistics, Oxford University Press
SS09 2217: Regresi Nonparametrik
MATA KULIAH/
SS09 2217: Nonparametrics Regression
COURSE TITLE
Credits: tiga/three Semester: III
Mengetahui beberapa model regresi nonparametrik, khususnya peran dan sifat-sifatnya. Dapat memodelkan perilaku data berdasarkan pendekatan regresi
TUJUAN
PEMBELAJARAN/ nonparametrik. ITS : 2009-2014
LEARNING
[ To know and understand various nonparametric
OBJECTIVES regression models, especially the uses and its
rriculum Cu
characteristics. Capable to modelling data behaviours based on nonparametric regression approach .]
Memahami konsep percobaan random, variabel
COMPETENCY
ekspektasi, konvergensi variabel random, model- model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]
x Konsep dasar regresi nonparametrik dan perbedaan dengan regresi parametrik. Estimasi densitas dengan
pendekatan histogram dan kernel. Estimasi kurva regresi nonparametrik dengan pendekatan kernel, deret ortogonal, spline, deret Fourier dan Wavelets.
POKOK
Pemilihan bandwith dalam regresi kernel, dan knot
BAHASAN/
pada regresi spine.
SUBJECTS
x [ Basical concept of nonparametric regression and the differences betwen nonparametric and parmetric
regression. Density estimation problems with histogram and kernel approach, orthogonal series, spline, Fourier series and wavelets. Bandwich selection in kernel regression, knot in spline regression ]
1. Enbank, R.L., 1988, Spline Smoothing and Nonparametric Regression, Marcel Dekker Ins, New York.
2. Green, P.J. and Silverman, B.W., 1994, Nonparametric Regression and Generalized Linear
Models, Chapman and Hall, London.
3. Hardle, W., 1990, Applied Nonparametric
PUSTAKA
UTAMA/
Regression, Cambridge University Press, New York.
Hardle, W., 1991, Smoothing Techniques With ITS : 2009-2014
REFERENCES
Implementation in S, Spinger Verlag, New York.
5. Prenter, P.M., 1975, Spline and Variational Methods, rriculum John Wiley and Sons, New York . Cu
6. Schumaker, L.L., 1981, Spline Functions: Basic Theory, John Wiley and sons, new York.
7. Thompson, J.R. and Tapia, R.A., 1990, Nonparametric
SIAM: Philadelpia.
8. Wahka, G., 1990, Spline Models for Observational Data, SIAM: Pensylvania.
SS09 2217: Analisis Deret Waktu
MATA KULIAH/
SS09 2217: Time Series Analysis
COURSE TITLE
Credits: tiga/three Semester: III
Memahami konsep-konsep statistika dalam model time series univariat (ARIMA), time series multivariat (Model Intervensi, Fungsi Transfer, dan VARIMA), dan Nonlinear time series. Dapat memodelkan time series univariat,
TUJUAN
PEMBELAJARAN/ multivariat, dan nonlinear time series.
LEARNING
[ To understand the statistical concepts used in univariate OBJECTIVES
time series models (ARIMA), Multivariate time series models (Intervention models, Transfer funtion and VARIMA), non linear time series. Able to model univariate, multivariate and nonlinear time series .]
x Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random ITS : 2009-2014 x
[Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers,
rriculum Central Limit Theorem, and function of random variable.]
Cu
POKOK
x Konsep proses stasioner, autokorelasi dan
BAHASAN/
autokorelasi parsial. Regresi dengan error
SUBJECTS
berautokorelasi (regresi time series). Model ARMA, berautokorelasi (regresi time series). Model ARMA,
x [ Stationarity concept, autocorrelation and partial autocorrelation, Regression with autocrrelated error
(time series regression), ARMA, ARIMA, and Seasonal ARIMA. Intervantion Model and otlier detection. Single and Multiple input transfer function. GARCH, VARIMA and nonlinear time series models. ]
1. Brockwell, P.J. and Davis, R.A., 1991, Time Series: Theory and Methods, 2nd Edition, Springer-Verlag:
New York.
2. Box, G.E.P., Jenkins, G.M., and Reinsel, D., 1994, Time
PUSTAKA
UTAMA/
Series Analysis : Forecasting and Control; 2nd Edition,
REFERENCES
Holden Day: San Fransisco .
3. Christensen, R., 1991, Linear Models for Multivariate, Time Series and Spatial Data, Springer-Verlag, New York.
4. Priestley, M.B., 1981, Spectral Analysis and Time Series, Academic Press: London.
SS09 2219: Proses Stokastik
MATA KULIAH/
SS09 2219: Stochastics Process
COURSE TITLE
Credits: tiga/three Semester: III
ITS : 2009-2014 Memahami konsep-konsep probabilitas yang banyak
TUJUAN
digunakan dalam proses stokastik, rantai markov, proses rriculum PEMBELAJARAN/ input-output, perbedaan proses renewal dengan input-
Cu
LEARNING
output, dan Brownian motion. OBJECTIVES
[ To understand the probability concepts used in stocastic [ To understand the probability concepts used in stocastic
Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]
x Review probabilitas bersyarat dan hukum probabilitas total. Klasifikasi proses stokastik, rantai Markov, probabilitas transisi, klasifikasi ruang keadaan, dan distribusi seimbang. Proses Poisson, sifat-sifat proses Poisson, dan proses Poisson nonhomogen. Proses input-output (birth-death processes), proses renewal, martingales, random walk, Browman motion, proses
POKOK
difusi, dan penerapannya.
BAHASAN/
x Reviewing conditional probability and total probability law. Classification of stochastic process. Markov chain,transition probability, classification of condition space and balanced distribution. Poisson process and its properties. Non homogeny Poisson process. Input-Output process (birth-death processes), renewal process, martingales, random walk, Browman motion, diffusion process ant its application. ]
SUBJECTS
1. Heyman, D.D. and Sobel, M.J., 1996, Stochastic ITS : 2009-2014 Models in Operations Research, Vol. 1, McGrraw-Hill,
PUSTAKA
New York .
UTAMA/
2. Kulkarni, V.G., 1998, Modeling, Analysis, Design, and rriculum
REFERENCES
Control of Stochastic System, Springer . Cu
3. Lawler, G.F., 2006, Introduction to Stochastic Process, Chapman and Hall.
Discrete Stochastic Dynamic Programming, John Wiley & Sons.
5. Ross, S.N., 1996, Stochastic Processes, John Wiley & Sons, New York.
6. Rolsky, T., Schmidt, H., Schmidt, V., and Tengels, J., 1999, Stochastic Process for Insurance and Finance, John Wiley & Sons .
7. Lyuer, Y.D., 2002, Financial Engineering and Computation, Cambridge Univ. Press.
SS09 2221: Riset Operasi
MATA KULIAH/
SS09 2221: Operation Research
COURSE TITLE
Credits: tiga/three Semester: I
Memahami berbagai metode kuantitatif dalam riset operasi dan memiliki ketrampilan menerapkannya dalam
TUJUAN
PEMBELAJARAN/ dunia praktis.
[ To understand miscelanous Quantitative methodsin OBJECTIVES
LEARNING
Operation research and have an ability to apply it on pratice .].
x Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
ITS : 2009-2014 model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence
rriculum of random variables, probability models, Law of Large Numbers,
Cu Central Limit Theorem, and function of random variable.]
x Analisis Jaringan, Ruang lingkup riset operasi, program
POKOK
linier: formulasi masalah, metode simplek primal,
BAHASAN/
SUBJECTS
ekonomik. Program bilangan bulat: teknik penca- bangan dan pembatasan, program bilangan campuran, program bilangan biner. Program dinamik: deterministik, probabilistik. Goal programing: single dan multiple. Teori permainan: strategi murni, campuran. Sistem antrian: antrian non poisson, antrian dengan disiplin prioritas, antrian dua phase. Program Nonlinear.
x [ Network Analysis, Coverage of operation Research, Linear programming: problems formulation, Prime
Simlex Methods, Dual, Revition, Pascal Opyimum analysis, sensitivity, economic’s interpretation. Integer Number’s program: Boundary and Branchery problems, mixed numbers program, binary numbers program. Dynamic program: deterministic and probabilistic. Goal programing: single dan multiple. Game Theory: Pure Strategy, mixed. Queueing System: Non Poisson Queueing, Queueing with Disipline Priority, Two Phase queuein. Nonlinar programming. ]
PUSTAKA
8. Hiller, F. and Lieberman, G.J., 1990, Introduction to
th
UTAMA/
Operation Research, 5 edition, McGrraw-Hill, New
REFERENCES
York .
9. Taha, H.A., 1973, Operation Research: An Introduction, Prentice Hall .
SS09 2223: Statistik Proses Kontrol ITS : 2009-2014
MATA KULIAH/
SS09 2223: Statistical Process Control
COURSE TITLE
Credits: tiga/three rriculum Semester: II
Cu
TUJUAN
Mampu melakukan pengontrolan proses multivariat dan PEMBELAJARAN/ memiliki potensi untuk mengembangkan metode baru
OBJECTIVES dalam pengontrolan proses.
[ Capable to control multivariate process and have potential capability to develop a new methods on controlling process .].
x Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]
x Pengantar Statistical Process Control. Diagram kontrol sederhana untuk atribut dan variabel. Diagram kontrol multivariat untuk atribut, target, dan
variabilitas. Indeks kemampuan proses: univariat dan multivariat. Diagram kontrol lain: CuSum, EWMA,
POKOK
Multiple Stream, Short Run, MCuSum, MEWMA,
BAHASAN/
Systematic Pattern.
SUBJECTS
x [ Introduction to Statistical Process Control. Simple control chart for atributes and variables. Multivariate control chart for attributes, targets and variability. Proces capacity Index: univariate and multivariate. Ohers control charts suh as: CuSum, EWMA , Multiple Stream, Short Run, MCuSum, MEWMA, Systematic Pattern. ]
ITS : 2009-2014
1. Montgomery, D.C., 2005, Introduction to Statistical
ed
Quality Control 5 , John Wiley and Sons, USA .
PUSTAKA
rriculum
UTAMA/
2. Fuch, C., Kennet, S.R., 1998, Multivariate Quality Cu
REFERENCES
Control, Theory and Application, Marcel Dekker Inc., New York
3. Lenz, H.J., Wilrich, P.T., 2004, Frontier in Statistical Quality Control 7, A Springer Verlag Co., Berlin . Kurikulum/
4. Keats, J.B., Hubele, N.F., 1989, Statistical Process Control in Automated Manufacturing, Marcel Dekker
Inc., New York .
5. Quesenberry, C.P., 1997, SPC Methods For Quality Improvement, John Wiley and Sons, USA .
6. Journal of Quality Technology, Journal of Quality Engineering, Tecnometrics.
SS09 2224: Teori Antrian
MATA KULIAH/
SS09 2224: Queueing Theory
COURSE TITLE
Credits: tiga/three Semester: II
Memahami konsep proses Markov dan kaitannya dengan teori antrian, sistem antrian, sistem antrian Markov, sistem antrian Semi Markov, sistem antrian jaringan terbuka, sistem antrian jaringan tertutup, dan Markov
TUJUAN
Modulated Arrival Process.
PEMBELAJARAN/ LEARNING
[ To understand the concept of Markov’s process and its OBJECTIVES relationship with Queueing Theory , Queueing System,
Markov’s queueing system, Semi markov’s queueing system, Opened network’s queueing system, closed network’s queueing system, Markov Modulated Arrival Process .]
ITS : 2009-2014 x
Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
rriculum
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema Cu
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence
Central Limit Theorem, and function of random variable.] x Review proses Markov diskrit dan kontinyu. Momen klaster sistem antrian, notasi Kendall, teorema little,
traffic intensity, dan hukum aliran konservasi. Sistem antrian Markov jalur tunggal dan ganda. Sistem antrian semi-Markov. Sistem antrian dengan prioritas. Sistem antrian M/G/I, dan G/M/I. Sistem antrian jaringan terbuka, teorema Burke, antrian jaringan Jackson, antrian jaringan tertutup, algoritma konvalensi, mean value analysis, Markov-modulated Poisson Process, Markov-modulated Bernoulli
POKOK
Process, dan Markov-modulated Fluid Flow
BAHASAN/ SUBJECTS
x [ Reviewing continue and discrete, Clustered moment of queing system, Kendall’s notation, Little Theorem, Traffic intensity, Law of conservation flow. Single and Multiple tracks of Markov Queueing system. Semi markov queueing system. Queueing system with priority. M/G/I and G/M/I queueing system . Opened network’s queueing system, Burke’s theorem. Jackson’s network’s queueing system. closed network’s queueing system. Convalention Algorithmmean value analysis, Markov-modulated Poisson Process, Markov-modulated Bernoulli Process, dan Markov-modulated Fluid Flow. ]
7. Breuer, L. And Baum, D., 2005, An Introduction to
PUSTAKA
UTAMA/
Queueing Theory and Matrix-Analytic Methods,
REFERENCES
Springer: Netherlands.
8. Tijms, H.C., 2003, A First Subjects/Course in ITS : 2009-2014 Stochastic Models, John Wiley & Sons: England.
rriculum SS09 2225: Perancangan Kualitas
Cu
MATA KULIAH/
SS09 2225: Quality Design
COURSE TITLE
Credits: tiga/three Semester: II
Mampu mendesain kualitas yang kokoh dan
TUJUAN
mengoptimumkan respon.
PEMBELAJARAN/ LEARNING
[ Orthogonal Arrays, Loss function, S/N ratio optimization OBJECTIVES for static and dynamic quality characteristic, optimization
of single and multiple respons .] x
Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]
x Orthogonal Arrays, Loss function, Optimasi S/N ratio untuk karakteristik kualitas yang statis dan dinamis,
POKOK
Optimasi respon tunggal dan ganda
BAHASAN/
x [ Orthogonal Arrays, Loss function, S/N ratio
SUBJECTS
optimization for static and dynamic quality characteristic, optimization of single and multiple respons. ]
PUSTAKA UTAMA/
1. Park, S.H., 1996, Robust Design and analysis for
REFERENCES
Quality Engineering, Chapman Hall.
2. Peace, G.S., 1993, Taguchi Methods, Addison Wesley.
ITS : 2009-2014
SS09 2226: Analisis Realibilitas
MATA KULIAH/
SS09 2226: Reliability Analysis rriculum
COURSE TITLE
Credits: tiga/three Cu Semester: III
TUJUAN
Memahami konsep-konsep Statistik yang banyak
LEARNING
digunakan dalam analisis reliabilitas, distribusi OBJECTIVES probabilitas dalam analisis reliabilitas, model regresi
untuk data reliabilitas, proportional Hazard Model, dan model Bayes.
[ To understand statistical concepts that have been used in reliability analysis, pobability density in reliability analysis, regression models for reability data, proportional Hazard Model, and Bayes Models .]
x Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-
KOMPETENSI/
model probabilitas, hukum bilangan besar, teorema
COMPETENCY
limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]
x Konsep laju kerusakan dan reliabilitas. Model eksponensial, gamma, weibull, normal, log normal, nilai ekstrim, dan model gabungan. Penaksiran parameter dan fungsi reliabilitas untuk sampel lengkap dan tersensor. Uji hipotesis, plot q-q, reliabilitas sistem pendekatan proses Markov, dan availiabilitas. Model regresi parametrik dan non
POKOK
parametrik, model multivariate dan stokastik, serta
BAHASAN/
metode Bayes.
SUBJECTS
x [ The concept of failure velocity and reliability. Exponential model, gamma, weibull, normal, log
ITS : 2009-2014 normal, extreme value, and joint models. Parameter
estimation and reliability function for both small and rriculum
large samples and cencored samples. Statistical Cu hyphotesis, q-q plot, Markov proces approximation
reliability systems and aviability. Parametric and non parametric regression, multivariate models and
1. Gertzbalck, I.B., 1989, Statistical Reliability Theory,
PUSTAKA
Marcell Decker, New York.
UTAMA/
2. Lawless, J.F., 1982, Statistical Models and Methods
REFERENCES
for Life Time Data, John Wiley & Sons: New York.
3. Sinha, S.K. and Kale, B.K., 1980, Life Testing and Reliability Estimation, Wiley Eastern LTD: New Delhi.
SS09 2231: Teknik Simulasi
MATA KULIAH/
SS09 2231: Simulation Techniques
COURSE TITLE
Credits: tiga/three Semester: I
Mampu membangun algoritma pembangkit data statistik yang berdistribusi univariat maupun multivariat dan model statistika secara simulasi stokastik. Mampu menggunakan simulasi stokastik untuk estimasi densitas
TUJUAN maupun model statistika. PEMBELAJARAN/ LEARNING
[ Capable to construct the algorithm of generating
OBJECTIVES statistical data which heve univariate or multivariate
distribution and statistical models using stocasticcaly simulation. Capable to use stocastic simulation to
estimate the density or statistical models .] ITS : 2009-2014 x
Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi,
rriculum
ekspektasi, konvergensi variabel random, model- Cu
KOMPETENSI/
COMPETENCY
model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random
x [Understanding concept of random experiment, random variable,
Central Limit Theorem, and function of random variable.] x Pembangkit bilangan acak dan variabel acak berdistribusi. Simulasi steady-state, Integrasi Monte
Carlo, Simulasi Markov Chain, Markov Chain Monte
POKOK
Carlo (Algoritma Gibbs sampler dan Metropolis-
BAHASAN/
Hastings). Teknik reduksi varians.
SUBJECTS
x [ Random number and distributed random variable generator. Steady-state simulation, Monte Carlo Integration, Markov Chain Simulation, Markov Chain Monte Carlo (Gibbs sampler algorithm dan Metropolis-Hastings). Variance reduction technique. ]
1. Asmussen, S. and Glynn, P.W., 2007, Stochastic
Simulation: Algorthms and Analysis.
PUSTAKA
UTAMA/
2. Law, A. And Kelton, C., 2000, Simulation Modelling
REFERENCES
and Analysis, McGraw-Hill.
3. Trivedi, K.S., 1982, Probability and Statistics with Reliability, Queueing and Computer Science Application, Addison Wesley.
SS09 2232: Metode Resampling
MATA KULIAH/
SS09 2232: Resampling Methods
COURSE TITLE
Credits: tiga/three Semester: I
Mampu membangun algoritma perbanyakan data yang terbatas
dengan resampling, baik data univariat maupun multivariat, serta baik ITS : 2009-2014
TUJUAN
PEMBELAJARAN/
secara uniform maupun secara terbobot dengan suatu densitas.
LEARNING
[Capable to construct the algorithm of generating a finite numbers of
data using resampling, univariatly or multivariatly, uniformly or rriculum
OBJECTIVES
Cu weightly with a fixed density]
x Mampu membangun algoritma perbanyakan data yang terbatas
KOMPETENSI/
dengan resampling, baik data univariat maupun multivariat, serta
COMPETENCY
baik secara uniform maupun secara terbobot dengan suatu baik secara uniform maupun secara terbobot dengan suatu
weightly with a fixed density] x Jacknife, Bootstrap, Generalized Bootstrap, Adaptive-Acceptance
POKOK
Rejection, Iterasi Full Conditional Distribution, Algoritma
BAHASAN/
Ekspektasi-Maksimisasi (EM).
SUBJECTS
x [Jacknife, Bootstrap, Generalized Bootstrap, Adaptive-Acceptance Rejection, Full Conditional Distribution Iteration, Expectation-
maximisation Algorithm (EM)]
1. Efron, B. and Tibhsirani, C., 1993, Bootstrap and Jacknife Method, John Wiley & Sons: New York.
PUSTAKA
2. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., 1995, Bayesian Data Analysis, Chapman & Hall, London.
UTAMA/
3. Tanner, M.A., 1996, Tools for Statistical Inference: Methods for
REFERENCES
the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition, Springer-Verlag: New York.
SS09 2233: Analisis Bayesian
MATA KULIAH/
SS09 2233: Bayesian Analysis
COURSE TITLE
Credits: tiga/three Semester: II/III
Mahasiswa mengerti, memahami dan menguasai teori Bayesian dan
TUJUAN
Empirical Bayes serta mampu mengaplikasikannya ke dalam permasa-
PEMBELAJARAN/
lahan real.
LEARNING
[The student can understand and menguasai the Bayesian theory and
OBJECTIVES
Empirical Bayes and capable to apply in to real-life problems.] x
Mahasiswa mengerti, memahami dan menguasai teori Bayesian
KOMPETENSI/
dan Empirical Bayes serta mampu mengaplikasikannya ke dalam permasa-lahan real.
COMPETENCY
x [The student can understand the Bayesian theory and Empirical Bayes and capable to apply in to real-life problems]
x Teorema Bayes, Bayesian inference, Data augmentation, Single- ITS : 2009-2014 parameter model, Multi-parameter model, Hirarchical model, Jenis prior, prior odds, posterior, posterior odds, Bayes faktor,
POKOK
Bayesian non-Normal dan neo-Normal model, Bayesian rriculum
BAHASAN/
Reliability, Mixture densitas, Mixture regresi, Mixture of mixture, Cu Pemilihan model terbaik dengan Bayesian, Struktur Perkalian