属性数据的多变量对应分析模型及应用

属性数据的建模和分析是市场研究活动中最重要的一类应用问题,学术界和市场研究行业一般使用列联表及统计检验的方法进行分析。故将法国统计学家J.P.Beozecri提出的对应分析法导入市场研究实证过程,使问卷调查中常见的多变量属性数据分析建模过程呈现出稳健的统计结论和直观的二维图示,并推广到广义多变量对应分析场合,其结论可靠,有应用创新。     

非农就业领域研究热点分析——基于CNKI数据库CSSCI期刊文献中关键词的共词分析

随着中国城市化进程的推进,农业对劳动力吸纳的能力不断下降,农村劳动力非农就业也变得越来越重要。以CNKI数据库中2000-2012年非农就业领域相关的CSSCI期刊论文为基础,以非农就业为主题检索所得期刊文献的关键词为研究对象,以共词分析法为研究方法,利用统计分析软件SPSS软件进行分析,得到中国非农就业领域的研究现状和研究热点。     

Heterogeneity's Ruses: Some Surprising Effects of Selection on Population Dynamics

As a cohort of people, animals, or machines ages, the individuals at highest risk tend to die or exit first. This differential selection can produce patterns of mortality for the population as a whole that are surprisingly different from the patterns for subpopulations or individuals. Naive acceptance of observed population patterns may lead to erroneous policy recommendations if an intervention depends on the response of individuals. Furthermore, because patterns at the individual level may be simpler than composite population patterns, both theoretical and empirical research may be unnecessarily complicated by failure to recognize the effects of heterogeneity.     

A Note on the Multivariate General Linear Test

The general linear test approach for testing hypotheses concerning parameters of a full-rank multivariate regression model is often not sufficiently discussed in applied multivariate analysis textbooks. This note makes mention of this approach and suggests that major emphasis of this topic in an applied multivariate analysis course provides students with a unifying structure for the analysis of statistical models.     

Distributional Studies and the Computer: An Analysis of Durbin's Rank Test

A study of the distribution of a statistic involves two major steps: (a) working out its asymptotic, large n, distribution, and (b) making the connection between the asymptotic results and the distribution of the statistic for the sample sizes used in practice. This crucial second step is not included in many studies. In this article, the second step is applied to Durbin's (1951) well-known rank test of treatment effects in balanced incomplete block designs (BIB's). We found that asymptotic, χ², distributions do not provide adequate approximations in most BIB's. Consequently, we feel that several of Durbin's recommendations should be altered.     

Editor's Notes

Proceedings of the Third International Conference on Teaching Statistics Editor David Vere-Jones; assistants: Shelley Carlyle and Brian P. Dawkins. Published by the International Statistical Institute. Available from ISI Permanent Office, 428 Prinses Beatrixlaan, P.O. Box 950, 2270 AZ Voor-burg, The Netherlands. $38 (U.S.), including postage and handling. Reviewed by Elliot A. Tanis     

The evaluation of trivariate normal probabilities defined by linear inequalities

This paper considers the evaluation of probabilities which are defined by a set of linear inequalities of a trivariate normal distribution. It is shown that these probabilities can be evaluated by a one-dimensional numerical integration. The trivariate normal distribution can have any covariance matrix and any mean vector, and the probability can be defined by any number of one-sided and two-sided linear inequalities. This affords a practical and efficient method for the calculation of these probabilities which is superior to basic simulation methods. An application of this method to the analysis of pairwise comparisons of four treatment effects is discussed.     

Multivariate Birnbaum–Saunders regression model

In this paper, we propose a multivariate log-linear Birnbaum–Saunders regression model. We discuss maximum-likelihood estimation of the model parameters and provide closed-form expressions for the score function and for Fisher's information matrix. Hypothesis testing is performed using approximations obtained from the asymptotic normality of the maximum-likelihood estimator. Some influence methods, such as the local influence and generalized leverage are discussed and the normal curvatures for studying local influence are derived under some perturbation schemes. Further, a test for the homogeneity of the shape parameter of the multivariate regression model is investigated. A real data set is presented for illustrative purposes.     

Nonparametric Random Variate Generation Using a Piecewise-Linear Cumulative Distribution Function

The standard approach to solving the interpolation problem for a trace-driven simulation involving a continuous random variable is to construct a piecewise-linear cdf that fills in the gaps between the data values. Some probabilistic properties of this estimator are derived, and three extensions to the standard approach (matching moments, weighted values, and right-censored data) are presented, along with associated random variate generation algorithms. The algorithm is a nonparametric blackbox variate generator requiring only observed data from the user.     

A generalized Gumbel distribution and its parameter estimation

ABSTRACT

In this article, main characteristics of a generalized Gumbel (GG) distribution are derived. Parameter estimation with method of moments, maximum likelihood, and Bayesian approaches are demonstrated. Due to the ranges of its skewness and kurtosis, it is satisfactory for fitting a wide variety of datasets. Also, it can be used to model block maxima or minima data due to its close connection with the standard Gumbel distribution. It is demonstrated that the GG distribution fits more accurately than both of the standard Gumbel and generalized extreme value distributions to block maxima data under specific conditions.