Bulletin of the Computational Statistics of Japan
Online ISSN : 2189-9789
Print ISSN : 0914-8930
ISSN-L : 0914-8930
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Volume 31, Issue 2
Displaying 1-12 of 12 articles from this issue
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Papers
  • SOME MATHEMATICAL PROPERTIES OF THE AGsi-curve — A NEW GRAPHICAL METHOD FOR CLASSIFYING SPATIAL POINT PATTERNS —
    Tsutomu Takai, Yoshiyasu Tamura, Hitoshi Motoyama
    2018 Volume 31 Issue 2 Pages 77-99
    Published: 2018
    Released on J-STAGE: March 25, 2020
    DOIhttps://doi.org/10.20551/jscswabun.31.2_77
    JOURNAL FREE ACCESS
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    　This article presents some mathematical properties of the AGsi-curve (AGglomerative-curve using single-linkage, Takai et al., 2017), which is used for graphical classification of spatial point patterns. Although, the AGsi-curves are constructed from the lengths of the Graph Edges of the Minimum Spanning Tree (GEMST), the exact distributions of the GEMST have not been established at this time. In this article, two kinds of approximate distributions of the AGsi-curve are established from deriving the approximate distribution of the GEMST in the multidimensional space; the exact distribution is also evaluated using the Monte Carlo method. Comparing the approximate and exact distributions reveals that two approximate distributions are useful in a limited range of intensity (the expectations of the Monte Carlo distributions exist between these two distributions) and that the edge effect of the minimum spanning tree affects the AGsi-curve.
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  • MODEL ASSISTED DESIGN WEIGHT CALIBRATION BY OUTLYINGNESS
    Kazumi Wada, Hiroe Tsubaki
    2018 Volume 31 Issue 2 Pages 101-119
    Published: 2018
    Released on J-STAGE: March 25, 2020
    DOIhttps://doi.org/10.20551/jscswabun.31.2_101
    JOURNAL FREE ACCESS
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    　In this paper, we propose a new design weight calibration method in sample surveys based on robust regression. Classical survey statistics may adopt the Horvitz-Thompson (HT) estimator to have finite population quantities such as mean and total. Design weights used for the estimator are the inverse of inclusion probabilities. They may cause a problem when there is any extreme value with large design weight.
    　The proposed calibration method utilizes a regression model explaining the target variable, and then estimates the parameters by any robust regression method which weight each record based on its outlyingness. We chose M-estimators and GM-estimators for evaluating accuracy improvement regarding finite population estimation. The weights derived from robust regression indicate outlyingness of each record. Calibrating design weights with those robust regression weights yields a new concept of survey weights considering both the sample design and outlyingness. We made Monte Carlo simulation with random number datasets and real datasets. The results are favorable and the weight calibration using M-estimators provides more efficient estimates for the finite population mean than that of GM-estimators.
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