Author
Nobuhiro Taneichi
Other affiliations: Obihiro University of Agriculture and Veterinary Medicine
Bio: Nobuhiro Taneichi is an academic researcher from Kagoshima University. The author has contributed to research in topics: Multinomial distribution & Goodness of fit. The author has an hindex of 5, co-authored 22 publications receiving 78 citations. Previous affiliations of Nobuhiro Taneichi include Obihiro University of Agriculture and Veterinary Medicine.
Papers
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TL;DR: In this article, Cressie and Read introduced a class of multinomial goodness-of-fit statistics Ra based on power divergence, and proposed a new approximation of the power of Ra called the approximation AE approximation.
15 citations
TL;DR: In this article, the authors derived an expression of approximation for the distribution of C φ under the hypothesis of independence, and then they proposed transformations that improve the speed of convergence to the chi-square limiting distribution.
11 citations
TL;DR: In this article, the authors used multivariate Edgeworth expansion for a continuous distribution and showed how the approximation based on the limiting normal distribution of Ra under nonlocal alternatives can be improved.
8 citations
TL;DR: An approximation is derived based on the continuous term of the asymptotic expansion for the distribution of the φ-divergence statistic under local alternatives that performs better than the other approximations.
Abstract: On the goodness-of-fit test for multinomial distribution, Zografos et al. (1990) proposed the φ-divergence family of statistics, which includes the power divergence family of statistics as a special case. They showed that under null hypothesis, the members of the φ-divergence family of statistics all have an asymptotically equivalent chi-square distribution. Furthermore, Menendez et al. (1997) derived an asymptotic expansion for the null distribution of the φ-divergence statistic. In this paper, we derive an approximation for the distribution of the φ-divergence statistic under local alternatives. The approximation is based on the continuous term of the asymptotic expansion for the distribution of the φ-divergence statistic. By using the approxi- mation, we propose a new approximation for the power of the statistic. The results are generalizations of those derived by Taneichi et al. which discussed the power divergence statistic. We numerically investigate the accuracy of the approximation when two types of concrete φ-divergence statistics are applied. By the numerical investigation, we find that the present approximation performs better than the other approximations.
7 citations
TL;DR: In this article, a concrete normalizing transformation is derived on the basis of Konishi's study of normalizing transformations, and some applications of the proposed transformation are shown, and performance of the transformation in the applications is numerically investigated.
Abstract: On the basis of Konishi's study of a normalizing transformation (Konishi [1]), a concrete normalizing transformation is derived. Some applications of the proposed normalizing transformation are shown, and performance of the transformation in the applications is numerically investigated.
5 citations
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Book•
01 Jan 1985
106 citations
TL;DR: This study investigates intelligent green building policies and the promotion of progress in Taiwan from 1988 to 2014 and finds key success factors are derived from analyzing and summarizing Intelligent green building experiences in Taiwan.
45 citations
TL;DR: The implicit knowledge of energy consumption characteristic obtained by the Waikato Environment for Knowledge Analysis analysis can be used to provide the owners with accurate predicted energy consumption performance to optimize architectural space, business equipment and operations management mode.
41 citations
01 Jun 2004
30 citations
TL;DR: In this article, a CUSUM scheme by transformation was proposed to monitor the time between events (TBE) data, which is more effective than monitoring the fraction non conforming directly.
Abstract: Time Between Events (TBE) charts were proposed to monitor the time between events occur based on exponential distribution, and have been shown to be more effective than monitoring the fraction non conforming directly. In this article, we consider monitoring the TBE data with CUSUM scheme by transformation. The idea behind it is to transform the TBE data to normal, and then apply the CUSUM scheme for the approximate normal data. Several simple transformation methods are examined. The calculation of Average Run Length (ARL) with Markov chain approach is described. Comparative studies on the ARL performance show that the transformed CUSUM is superior to the X-MR (Moving Range) chart with transformation, the Cumulative Quantity Control (CQC) chart, and have comparable performance with exponential CUSUM charts. The design procedures of optimal CUSUM chart are also presented. This study provides another possible alternative for monitoring TBE data with easy design procedures and relatively good performance.
25 citations