ASYMPTOTIC DISTRIBUTION OF LIKELIHOOD RATIO STATISTIC FOR TESTING SPHERICTY IN GROWTH CURVE MODELS

1IntroductionTheideaofgeneralizedgrowthcurvemodelsmeanswhereY=(yi,y2,''tAN)isapxNobservationmains;bothBandAarematrixeswithknownconstantelementsoforderpxqandmxNrespectively:(isaqxinparametermatriX;e=(el,ez,'',eN)isarandomerrorrnatrisoforderNxN;Zisanonnegativedefitiveparametermatrixoforderpxp,W=(wij)isaNxNnon-negativedefitivernatriswithknownelements.LetYdenoteamatriXwhosecoluxnnvectorsarethecorrelatedsaxnpleswithsizeNfroma"dimensionalnormalpopulation.Thecovariancematrixofthepopulationis…     

Testing for independence by the empirical characteristic function

Tests of total independence of d (≥2) random variables are proposed using the empirical characteristic function. The approach is parallel to that of Hoeffding, Blum, Kiefer, and Rosenblatt.     

TESTING SPHERICITY IN A GMANOVA-MANOVA MODEL WITH NORMAL ERROR

This article presents a statistic for testing the sphericity in a GMANOVA- MANOVA model with normal error. It is shown that the null distribution of this statistic is beta and its nonnull distribution is given in series form of beta distributions.     

One‐way analysis of variance with long memory errors and its application to stock return data

Recent empirical results indicate that many financial time series, including stock volatilities, often have long‐range dependencies. Comparing volatilities in stock returns is a crucial part of the risk management of stock investing. This paper proposes two test statistics for testing the equality of mean volatilities of stock returns using the analysis of variance (ANOVA) model with long memory errors. They are modified versions of the ordinary F statistic used in the ANOVA models with independently and identically distributed errors. One has a form of the ordinary F statistic multiplied by a correction factor, which reflects slowly decaying autocorrelations, that is, long‐range dependence. The other is a test statistic such that the degrees of freedom of the denominator in the ordinary F test statistic is calibrated by the so‐called effective sample size. Empirical sizes and powers of the proposed test statistics are examined via Monte Carlo simulation. An application to German stock returns is presented. Copyright © 2007 John Wiley & Sons, Ltd.     

伽玛分布族参数的经验Bayes双边检验的收敛速度:NA样本情形

本文研究了NA样本情形下,伽玛分布族形状参数的经验Bayes(EB)双边检验问题.利用概率密度函数的核估计,构造了参数的经验Bayes检验函数,并在适当的条件下,证明了所提出的经验Bayes检验函数的渐近取优(a.o.)性,获得了其收敛速度.     

Locally minimax tests in symmetrical distributions

In this paper we give an extension of the theory of local minimax property of Giri and Kiefer (1964, Ann. Math. Statist., 35, 21–35) to the family of elliptically symmetric distributions which contains the multivariate normal distribution as a member.This work was partially supported by the Canadian N.S.E.R.C. grant     

Some test statistics based on the martingale term of the empirical distribution function

Summary It is proved that the martingale term of the empirical distribution function converges weakly to a Gaussian process inD[0, 1]. Some statistics for goodness-of-fit tests based on the martingale term of the empirical distribution function are proposed. Asymptotic distributions of these statistics under the null hypothesis are given. The approximate Bahadur efficiencies of the statistics to the Kolmogorov-Smirnov statistic and to the Cramér-von Mises statistic are also calculated. The Institute of Statistical Mathematics     

On the inadmissibility of preliminary-test estimators when the loss involves a complexity cost

Summary Estimation-preceded-by-testing is studied in the context of estimating the mean vector of a multivariate normal distribution under squared error loss together with a complexity cost. It is shown that although the preliminary test estimator is admissible for the univariate problem (cf Meeden and Arnold (1979),J. Amer. Statist. Assoc.,74, 872–874), for dimensionp≧3, the estimator is inadmissible. A new preliminary test estimator is obtained, which depends on the cost for each component and dominates the usual preliminary test estimator. Research partially supported by the NSF Grant Number DMS-82-18091 and partially by the DSR Research Development Award, University of Florida.     

上海股市波动的周日效应检验

与以往日历异常现象的研究大多集中在股市收益率上不同,本文对上海股市波动的周日效应进行实证研究,无条件波动的修正Levene检验和条件波动的GARCH模型被应用。结果显示上海股市存在显著的星期一高波动现象,利用混合分布模型对此现象进行了解释,周末信息的积累对星期一交易的影响可能是其高波动的原因。