著者

Koike Ken-ichi Akahira Masafumi

出版者

Marcel Dekker

雑誌

Sequential analysis (ISSN:07474946)

巻号頁・発行日

vol.12, no.3-4, pp.211-218, 1993

被引用文献数

1

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In the sequential multinomial sampling case,a sufficient condition for a non-randomized sequential procedure to be complete is given,and also a necessary and sufficient condition for a randomized sequential procedure to be complete is obtained.

著者

Koike Ken-ichi

出版者

Taylor & Francis

雑誌

Sequential analysis (ISSN:07474946)

巻号頁・発行日

vol.26, no.1, pp.63-70, 2007

被引用文献数

4 5

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For a location-scale parameter family of distributions with a finite support, a sequential confidence interval with a fixed width is obtained for the location parameter, and its asymptotic consistency and efficiency are shown. Some comparisons with the Chow-Robbins procedure are also done.

著者

Aoshima Makoto Yata Kazuyoshi

出版者

Taylor & Francis

雑誌

Sequential analysis (ISSN:07474946)

巻号頁・発行日

vol.30, no.4, pp.432-440, 2011-11

被引用文献数

6

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In this article, we respond to the comments made by the 10 discussants on “Two-Stage Procedures for High-Dimensional Data.” We also give some new results along with their brief explanations.

著者

Aoshima Makoto Yata Kazuyoshi

出版者

Taylor & Francis

雑誌

Sequential analysis (ISSN:07474946)

巻号頁・発行日

vol.30, no.4, pp.356-399, 2011-11

被引用文献数

51 13

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In this article, we consider a variety of inference problems for high-dimensional data. The purpose of this article is to suggest directions for future research and possible solutions about p n problems by using new types of two-stage estimation methodologies. This is the first attempt to apply sequential analysis to high-dimensional statistical inference ensuring prespecified accuracy. We offer the sample size determination for inference problems by creating new types of multivariate two-stage procedures. To develop theory and methodologies, the most important and basic idea is the asymptotic normality when p → ∞. By developing asymptotic normality when p → ∞, we first give (a) a given-bandwidth confidence region for the square loss. In addition, we give (b) a two-sample test to assure prespecified size and power simultaneously together with (c) an equality-test procedure for two covariance matrices. We also give (d) a two-stage discriminant procedure that controls misclassification rates being no more than a prespecified value. Moreover, we propose (e) a two-stage variable selection procedure that provides screening of variables in the first stage and selects a significant set of associated variables from among a set of candidate variables in the second stage. Following the variable selection procedure, we consider (f) variable selection for high-dimensional regression to compare favorably with the lasso in terms of the assurance of accuracy and the computational cost. Further, we consider variable selection for classification and propose (g) a two-stage discriminant procedure after screening some variables. Finally, we consider (h) pathway analysis for high-dimensional data by constructing a multiple test of correlation coefficients.