The Outline of Dissertation Novelty

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1.4 The Outline of Dissertation

This dissertation is divided structured into six chapters as described in Figure 5. Chapter 1 is an introduction that contains the background, the purpose of research, the research framework, and the dissertation outline. Chapter 2 discusses a method of ranking, namely the Ordering Dually in Triangles ORDIT and its implementation on severity or poverty data from BPS. The method applied on data concerning the level of poverty in several sub-districts in Java is based on the number of statements of inability to pay and number of health insurance of the poor. Furthermore, the result of poverty ranking is grouped into 3 levels and used as ordinal response in modeling in Chapters 4. Figure 5 Research diagram: relation among chapters 14 Chapter 3 is a comparison between two hotspot detection methods, namely the Circle Based Hotspot Detection from Kulldorf 1997 and the Upper Level Set Scan Statistics from Patil and Taillie 2004 using the 14 criteria. The best method is used to detect bad nutrition cases in some districts and the result is used as an explanatory variable in the modeling Chapter 4 discusses the nested GLM and nested GLMM and their implementations on the poverty data from BPS. Chapter 5 is a general discussion of modeling in Chapter 4. Chapter 6 contains conclusions and recommendations and well a summary of the research in this dissertation.

1.5 Novelty

As described in Section 1.1, the model of Zhang and Lin 2008 was based on un-nested spatial area. It is important to develop a model for nested spatial, where the forms of correlations or variance-covariance matrix is based on the condition of the data considered. As an archipelago, Indonesia is an example of the nested spatial condition where lines, distance, and water act as borders that set off one area from another. When a hotspot emerges in such areas, we need to know how significant they are in generally. This question has been successfully answered by the development of a nested-spatial model. The novelty of this dissertation is the model that involves an interesting hotspots used as an explanatory variable and a spatial factor used as the random effect in nested area. This model is called as the Nested Generalized Linear Mixed Model NGLMM, a development from the Zhang and Lin’s model 2008. However, the spatial factor in this model is not given the same treatment as spatial factors in other spatial modeling. Spatially unstructured random effect is assumed to be identical independent, and normal distributed. The GEE approach is used for inference and is adapted for the nested spatial condition for GLM with a multinomial ordinal response variable which gives complexity to the parameter model estimation. 15

Chapter 2 SUB DISTRICTS POVERTY LEVEL DETERMINATION