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Volume 31 (2002)
- Issue 3 Pages 175-
- Issue 2 Pages 107-
- Issue 1 Pages 3-
Volume 31, Issue 2
Displaying 1-3 of 3 articles from this issue
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Manabu Kuroki, Masami Miyakawa2002 Volume 31 Issue 2 Pages 107-121
Published: November 30, 2002
Released on J-STAGE: June 12, 2009
JOURNAL FREE ACCESSIn case where causal relationships among variables can be described by a causal diagram and the corresponding linear structural equation model, this paper deals with statistical inference problems of joint intervention effects, which are the effects of external interventions in some treatment variables. The joint intervention effects defined by Pearl and Robins (1995) are formulated as a nonparametric distribution. This paper formulates both the mean and the variance of the joint intervention effects through the path coefficients in a linear structural equation model. In addition, it clarifies how linear regression models should be used in order to estimate both the mean and the variance of the joint intervention effects. Furthermore, this paper gives explicit expression of the mean and the variance of the joint intervention effects through linear regression parameters.View full abstractDownload PDF (685K) -
Tomohiro Ando, Junichiro Simauchi, Sadanori Konishi2002 Volume 31 Issue 2 Pages 123-139
Published: November 30, 2002
Released on J-STAGE: June 12, 2009
JOURNAL FREE ACCESSNeural networks have received considerable attention in various fields of research such as statistical science, engineering, computer science, artificial intelligence, among others. We considered the multi-class classification problem based on radial basis function networks with hybrid learning and the technique of regularization.View full abstractDownload PDF (2661K) -
Yasuhiko Takemoto, Ikuo Arizono2002 Volume 31 Issue 2 Pages 141-162
Published: November 30, 2002
Released on J-STAGE: June 12, 2009
JOURNAL FREE ACCESSThis article deals with the hypothesis test for the homogeneity of two normal populations. The likelihood ratio test is a usual method for such testing. In general theory, the asymptotic distribution of log-likelihood ratio statistics as testing statistics is well known under the null hypothesis, so we may design the test based on the asymptotic distribution. On one hand, the asymptotic distribution of log-likelihood ratio statistics is also considered under the alternative hypothesis. However, in the case that sample sizes are not so large, the asymptotic distributions are not so accurate under the arbitrary hypotheses. Recently, Arizono et al. have derived the cumulant generating function for the log-likelihood ratio statistics under the null hypothesis when the sample size is finite, and enabled to design accurately the null hypothesis test for homogeneity of two normal populations. However, the stochastic properties of log-likelihood ratio statistics under the alternative hypothesis and the power of testing have not been investigated in theory. Therefore, we consider the stochastic properties of log-likelihood ratio statistics under arbitrary hypotheses in this article. Concretely, the cumulant generating function for the log-likelihood ratio statistics and the cumulant of arbitrary order are derived under the arbitrary hypotheses when the sample size is finite. Further, by using the cumulants of log-likelihood ratio statistics derived in this article, we develop the approximation for the distribution of log-likelihood ratio statistics, and propose the theoretical evaluation of the power for the homogeneity hypothesis test.View full abstractDownload PDF (897K)
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