Journal of the Japan Statistical Society, Japanese Issue
Online ISSN : 2189-1478
Print ISSN : 0389-5602
ISSN-L : 0389-5602
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Volume 23, Issue 2
Displaying 1-13 of 13 articles from this issue
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  • Some Aspects of my Experience of Statistical Research Regarded as Useful even Today
    Presidential Address in the 61st Meeting of The Japan Statistical Society
    Minoru Siotani
    1993 Volume 23 Issue 2 Pages 115-121
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.115
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    I deem it my great honor to give this presidential address. Topics are chosen from my, experience in the research life of statistics at home and abroad with the hope that they are still instructive and suggestive for today's statistical situation in Japan. However, some topics were already written in author's paper [10], so the overlapping topics with them are avoided here.
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  • A GOODNESS OF FIT TEST BASED ON A CHARACTERIZATION OF UNIFORM DISTRIBUTION
    Takamasa Hashimoto, Shingo Shirahata
    1993 Volume 23 Issue 2 Pages 123-130
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.123
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    In this paper, we consider a test statistic for testing the goodness of fit of a completely specified null distribution. The statistic is based on a characterization of the uniform distributions. Since any continuous distributions can be transformed to the uniform distribution over the unit interval, it is consistent for any alternative distributions. The power properties of our test are not so excellent. However, since no test statistics based on characterizations arc known, our test will be worth considered.
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  • ESTIMATING A COVARIANCE MATRIX OF A NORMAL DISTRIBUTION WITH UNKNOWN MEAN
    Tatsuya Kubokawa, Toshio Honda, Kenji Morita, A. K. E. Saleh
    1993 Volume 23 Issue 2 Pages 131-144
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.131
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    For the covariance matrix of the multivariate normal distribution with an unknown mean vector, discontinuous or continuous Stein type truncated estimators have been proposed. This article summarizes a series of recent results and obtains an improved and generalized Bayes estimator based on the Brown-Brewster-Zidek method, well known in the univariate case. The asymptotic risk expansions of the estimators are derived, numerically investigated, and it is revealed that the risk-reductions of the generalized Bayes estimator and an empirical Bayes estimator are considerably great in the large dimensional case.
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  • IMPROVED ESTIMATION FOR THE COMPONENT MEAN-VECTOR
    S. Ejaz Ahmed, A. K. E. Saleh
    1993 Volume 23 Issue 2 Pages 145-159
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.145
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    This article discusses four estimation methods for the first component mean vector μ1 of a q-variate normal distribution when it is suspected that μ1=μ2, where μ2 is the second component mean vector. Exact bias and risks of all of these estimators are derived and their efficiencies relative to a classical estimators are studied. An optimum rule for the preliminary test estimator (PTE) is discussed. The range in the parameter space where preliminary test estimator dominates shrinkage estimator is investigated. It is shown that the Stein-rule estimator (SE) dominates the classical one, whereas none of the PTE and SE dominate each other. The range in the parameter space where PTE dominates SE is also investigated. It is found that SE outperforms the PTE except in a range around the null hypothesis. Further, for large values of α, the level of statistical significance, SE dominates the PTE uniformly. The relative dominance picture of the estimators is presented.
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  • CONFIDENCE BELTS FOR THE BINOMIAL PARAMETER
    Shiro Yamazoe
    1993 Volume 23 Issue 2 Pages 161-169
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.161
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    Exact confidence regions for the binomial parameter p were given by Sterne [9]. However, they are not always interval-valued. In this paper, Steme's confidence intervals are reanalyzed, and modified confidence intervals are presented. Our improved confidence intervals have the minimum total length and have the maximum acceptance probabilities among all confidence intervals with minimum total length. Ancfficient algorithm for the confidence intervals is also presented.
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  • ESTIMATION OF PROBABILITIES AFTER CLUSTERING
    Kai Fun Yu
    1993 Volume 23 Issue 2 Pages 171-181
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.171
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    Asample is taken from a mixture of two subpopulations. The characteristics of the two sub-populations are to be estimated. If each observation in the sample is completely identiFIed, that is, if one knows which observation comes from which sub-population, then the estimation can follow standard methods and it is straightforward. However, if the observations are not identified, then some clustering Procedure has to beapplied to classify the observations into two sub-populatiolls. A reasonable estimate turns out to be an inconsistent estimate as long as there is a chance of misclassification. This note introduces a general method of estimation after clustering. This estimation procedurc subsumes the reasonablc method when the identities of the observations are known.
    A concept of fuzzy partition is employed here to by-pass the problem of misclassi-fication. Two examples will be discussed. One example will involve a parametric classification procedure and the other will involve a nonparametric clustering procedure called K-means. A Monte Carlo study will be conducted to compare the estimates arising from a classical clustering procedure and a fuzzy clustering procedure.
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  • SHRINKAGE REGRESSION PREDICTORS FOR PREDICTION AREA DIFFERENT FROM SAMPLE AREA
    Takakatsu Inoue
    1993 Volume 23 Issue 2 Pages 183-199
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.183
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    In a linear regression model, some shrinkage type regression predictors are examined in a situation where a prediction area is different from a sample area. This is then evaluated from the perspective of an estimation error of shrinkage parameter to estimate from a sample. The goodness of the shrinkage regression predictor is appraised on the basis of the expectation of Prediction Mean Square Error (PMSE) with respect to the model condition parameter following a prior distribution. A modified shrinkage predictor is proposed.
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  • SOME ROBUSTNESS PROPERTIES OF THE SIMPLE EXPONENTIAL SMOOTHING PREDICTOR
    Chunhang Chen
    1993 Volume 23 Issue 2 Pages 201-214
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.201
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    Suppose we have the observations of a stochastic process {Xt} and we are required to predict its future values. There are many forecasting methods that might be used. However, it is difficult to decide which method we should adopt, since the accuracy of a forecasting method often depends on the properties of {Xt}, which can not be clearly known in many practical situations. In this paper, we investigate some aspects on robustness of the simple exponential smoothing method (SES). We will consider whether or not the forecasts provided by SES are reasonable not only when {Xt} is stationary but also when {Xt} deviates from a stationary process. For that purpose, we show prediction errors of the SES predictor for a wide class of stochastic processes, and we show comparisons of the prediction errors between this predictor and another predictor which is widely used.
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  • A CRITERION ON THE CHOICE OF A LEFT SIGNATURE AND A STRATIFIER OF GIBSON'S METHOD IN LATENT CLASS ANALYSIS WITH TWO CLASSES
    Tsukio Morita
    1993 Volume 23 Issue 2 Pages 215-222
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.215
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    It has been pointed out that the solutions in a latent class analysis are seriously unstable. There seems to exist two causes of the instability. One is a geometric structure in a latent class model and the other is a property of the methods for solving the latent structure equations. In the latter there are several methods that have been proposed so far. A robust method which copes with the instability of the model, however, has not, yet been proposed. In this paper we will only treat the latent class analysis with two classes. The aim is to suggest a criterion on the choice of a left signature and a stratifier of Gibson's method, so that the method may become a robust one. Moreover in order to get highly reliable solutions we shall give a new proposal to develop items, i.e., questionnaire. A numerical example will be given.
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  • Estimation of Type II Tobit Models
    Kazumitsu Nawata
    1993 Volume 23 Issue 2 Pages 223-247
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.223
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    Type II Tobit models are widely used in various feilds of economics, such as labor economics. These models are also known as models with sample-selection biases. Because of its computational difficulty, the maximum likelihood estimator (MLE) is seldom used to estimate these models, while Heckman's two-step estimator (Heckman [1976 and 1979]) is widely used to estimate these models. However, Heckman's two-step estimotor sometimes performs poorly and the MLE is known to be a better estimator. In this paper, I point out some of the limitation of Heckman's two-step estimator, and I compare the two estimators by the Monte Carlo experiments. I also present the computor program which makes possible to calculate the MLE.
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  • Nonstationarity Existing in Annual Maximum 1•2•3 Day (s) Precipitation Time Series and Estimation of Nonstationary Hydrologic Variable
    Noriaki Sogawa, Satoru Nakamura, Hiroki Yamada
    1993 Volume 23 Issue 2 Pages 249-262
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.249
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    [in Japanese]
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  • Book Reviews
    [in Japanese], [in Japanese], [in Japanese], [in Japanese], [in Japane ...
    1993 Volume 23 Issue 2 Pages 263-272
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.263
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  • Erratum to : TERNARY VARIANCE-BALANCED DESIGNS THROUGH BIB AND GD DESIGNS
    1993 Volume 23 Issue 2 Pages 279
    Published: 1993
    Released on J-STAGE: January 22, 2009
    DOIhttps://doi.org/10.11329/jjss1970.23.2_279
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