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Volume 35 (2006)
- Issue 3 Pages 139-
- Issue 2 Pages 79-
- Issue 1 Pages 1-
Volume 35, Issue 3
Displaying 1-3 of 3 articles from this issue
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Tatsuya Kubokawa2006 Volume 35 Issue 3 Pages 139-161
Published: March 30, 2007
Released on J-STAGE: June 12, 2009
JOURNAL FREE ACCESSSample survey data can be used to derive a reliable estimate of the total mean for a large area. When the same data are used to estimate means of small areas like a town, city, or county belonging to the large area, the usual direct estimators such as the sample mean have unacceptably large standard errors due to the small sizes of the samples in the small areas. This is known as the small area problem. To obtain more accurate estimates for given small areas, one needs to "borrow strength" from the related areas. The linear mixed model (LMM) is recognized as an appropriate model for handling such a problem, and the resulting empirical best linear unbiased predictor (EBLUP) can yield a smaller standard error. This article reviews small area estimation based on LMM. In particular, it explains how the structure of common parameters plus random effects in LMM works to get accurate estimates. The estimators of the mean squared errors of EBLUP and the confidence interval based on EBLUP are derived to evaluate the accuracy of EBLUP. Finally, some generalizations and various variants of LMM are described in order to analyze spatial data, and the generalized linear mixed model (GLMM) and its application to mortality rate estimation are explained.View full abstractDownload PDF (1089K) -
Masato Sugimoto, Shigeru Mase, Takayuki Sakaguchi, Makoto Suzuki, Mako ...2006 Volume 35 Issue 3 Pages 163-177
Published: March 30, 2007
Released on J-STAGE: June 12, 2009
JOURNAL FREE ACCESSWhen investigating underground geological structures, basic and direct data are boring data. But these are both costly and time-consuming. Cheaper but less exact global data can be obtained using electromagnetic waves. Researchers can detect specific resistance values by observing the reflections of electromagnetic waves transmitted from the surface of the earth or airplanes. Therefore, as to specific resistance values, we can observe three kinds of data, which are one-, two-, and three-dimensional ones respectively and become less exact in that order. We propose two new models which can synthesize these data, which have hierarchical structures, and also propose corresponding geostatistical predictors. Simulation studies are given here to show the effectiveness of proposed methods.View full abstractDownload PDF (3608K) -
Takanori Tanase, Shin-ichi Matsuda2006 Volume 35 Issue 3 Pages 179-194
Published: March 30, 2007
Released on J-STAGE: June 12, 2009
JOURNAL FREE ACCESSPearson's X2 test and Fisher's exact test are used for testing the independence of a contingency table, but these tests tell us only whether two factors in a row and column are associated. If we reject the null hypothesis of independence and want to know which two categories in a row/column are associated, we need the concept of multiple comparisons. We chose the Scheffé method and Tukey method in Hirotsu (1992) and the closed testing procedure in Matsuda (2004) as known multiple comparison procedures for contingency tables. We devised a new procedure assuming ordered alternative hypotheses and evaluated its performance against known methods. We found that our procedure is useful for ordered alternative hypotheses and that the closed testing procedure in Matsuda (2004) is useful for general alternative hypotheses.View full abstractDownload PDF (695K)
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