C73. The diminishing significance of a p-value as the sample size increases

Barnard (1963), Birnbaum (1974), and others have proposed exact inference procedures based on simulation. This article surveys existing results in this general area and fills several important holes, addressing philosophical problems and the matter of determining a simulation size. Applications to multiple comparisons, sequential analysis, and statistical consulting are discussed.     

A Distribution Free Test for the Equality of Scales

Situations where scale parameters are not nuisance factors to be controlled but outcomes to be explained arise in many contexts such as quality control, agricultural production systems, experimental education, the pharmaceutical industry and biology. Tests for homogeneity of variances are often of interest also as a preliminary to analysis of variance, dose-response modelling or discriminant analysis. The literature on tests for the equality of scales is vast. A test which usually stands out in terms of power and robustness against non normality is the modified Levene W50 test, however in the literature no test is found to be the most powerful one for every distribution. The goal of the article is to propose an effective method for comparing scales. More precisely, we propose a test for the equality of scales that, even though was not the most powerful one for every distribution, it has good overall performance under every type of distribution. This test has the form of a combined resampling test. It is important to note that non combined tests show good performance only in particular contexts. Size and power of the proposed test are studied via simulation and compared with many other robust tests for scale. A practical application to industrial quality control is discussed.     

On the role of minque in testing of hypotheses under hiked linear models

Testing of hypotheses under balanced ANOVA models is fairly simple and generally based on the usual ANOVA sums of squares. Difficulties may arise in special cases when these sums of squares do not form a complete sufficient statistic. There is a huge literature on this subject which was recently surveyed in Seifert's contribution to the book of Mumak (1904). But there are only a few results about unbalanced models. In such models the consideration of likelihood ratios leads to more complex sums of squares known from MINQUE theory.

Uniform optimality of testsusually reduces to local optimality. Here we prespnt a small review of methods proposed for testing of hypotheses in unbalanced models. where MINQUEI playb a major role. We discuss the use of iterated MINQUE for the construction of asymptotically optimal tests described in Humak (1984) and approximate tests based on locally uncorrelated linear combinations of MINQUE estimators by Seifert (1985), We show that the latter tests coincide with robust locally optimal invariant tests proposeci by Kariya and Sinha and Das and Sinha, if the number of variance components is two. Explicit expressions for corresponding tests are given for the unbalanced two-way cross classification random model, which covers some other models as special cases. A simulation study under lines the relevance of MINQUE for testing of hypotheses problems.     

Locally most powerful tests for the two sample problem when the combined sample is type ii censored

We derive locally most powerful tests for the two sample problem when the combined sample is type II censored. Both middle-censored and doubly-censored cases are considered. These include, in particular, the left- and right-censored cases. The main contribution of this paper is to provide extensive small sample size tables of critical values for Wilcoxon. normal scores, Freund-Ansari-Bradley and Capon tests.     

Divergence-based confidence intervals in false-positive misclassification model

In this article, we introduce minimum divergence estimators of parameters of a binary response model when data are subject to false-positive misclassification and obtained using a double-sampling plan. Under this set up, the problem of goodness-of-fit is considered and divergence-based confidence intervals (CIs) for a population proportion parameter are derived. A simulation experiment is carried out to compare the coverage probabilities of the new CIs. An application to real data is also given.     

Keep it simple: on specific-to-general predictor selection for time series forecasting in the short,medium and long run

This paper describes the forecasting performance of general-to-specific and specific-to-general predictor selection within specifications fitting into the class of (approximate) linear autoregressions. Short, medium and long horizon forecasting exercises are distinguished. Regarding the latter, iterative prediction is compared with direct conditioning on available time series information. Ex ante forecasting results are provided for 495 real macro-economic and financial time series recently collected for 25 economies and the Euro area [A. Inouea and L. Kilian, On the selection of forecasting models, J. Econ. 130 (2006), pp. 273–306]. Almost 9000 single predictions enter the modelling comparison. Overall, specific-to-general predictor selection turns out to offer preferable prediction outcomes in terms of statistical and more economic loss functions. With regard to medium (long) term prediction, the analysis is supportive for direct (iterative) multistep prediction.     

Data depth‐based nonparametric scale tests

Liu and Singh (1993, 2006) introduced a depth‐based d‐variate extension of the nonparametric two sample scale test of Siegel and Tukey (1960). Liu and Singh (2006) generalized this depth‐based test for scale homogeneity of k ≥ 2 multivariate populations. Motivated by the work of Gastwirth (1965), we propose k sample percentile modifications of Liu and Singh's proposals. The test statistic is shown to be asymptotically normal when k = 2, and compares favorably with Liu and Singh (2006) if the underlying distributions are either symmetric with light tails or asymmetric. In the case of skewed distributions considered in this paper the power of the proposed tests can attain twice the power of the Liu‐Singh test for d ≥ 1. Finally, in the k‐sample case, it is shown that the asymptotic distribution of the proposed percentile modified Kruskal‐Wallis type test is χ² with k ? 1 degrees of freedom. Power properties of this k‐sample test are similar to those for the proposed two sample one. The Canadian Journal of Statistics 39: 356–369; 2011 © 2011 Statistical Society of Canada     

Three centuries of categorical data analysis: Log-linear models and maximum likelihood estimation

The common view of the history of contingency tables is that it begins in 1900 with the work of Pearson and Yule, but in fact it extends back at least into the 19th century. Moreover, it remains an active area of research today. In this paper we give an overview of this history focussing on the development of log-linear models and their estimation via the method of maximum likelihood. Roy played a crucial role in this development with two papers co-authored with his students, Mitra and Marvin Kastenbaum, at roughly the mid-point temporally in this development. Then we describe a problem that eluded Roy and his students, that of the implications of sampling zeros for the existence of maximum likelihood estimates for log-linear models. Understanding the problem of non-existence is crucial to the analysis of large sparse contingency tables. We introduce some relevant results from the application of algebraic geometry to the study of this statistical problem.     

青海省旅游产品游客满意及忠诚的影响要素分析

文章对青海省旅游产品的构成要素进行分析,采用结构方程对旅游产品各要素的满意情况与游客总体满意和忠诚的关系进行分析,从而获得青海省旅游产品各要素的发展状况,为青海省旅游产品的发展和完善,以及旅游资源的规划和开发提供参考。