Geographically weighted kernel logistic regression for small area proportion estimation

- Journal title : Journal of the Korean Data and Information Science Society
- Volume 27, Issue 2, 2016, pp.531-538
- Publisher : Korean Data and Information Science Society
- DOI : 10.7465/jkdi.2016.27.2.531

Title & Authors

Geographically weighted kernel logistic regression for small area proportion estimation

Shim, Jooyong; Hwang, Changha;

Shim, Jooyong; Hwang, Changha;

Abstract

In this paper we deal with the small area estimation for the case that the response variables take binary values. The mixed effects models have been extensively studied for the small area estimation, which treats the spatial effects as random effects. However, when the spatial information of each area is given specifically as coordinates it is popular to use the geographically weighted logistic regression to incorporate the spatial information by assuming that the regression parameters vary spatially across areas. In this paper, relaxing the linearity assumption and propose a geographically weighted kernel logistic regression for estimating small area proportions by using basic principle of kernel machine. Numerical studies have been carried out to compare the performance of proposed method with other methods in estimating small area proportion.

Keywords

Geographically weighted regression;kernel machine;logistic regression;mixed effects model;small area estimation;

Language

English

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