Estimation of regression models with equi-correlated responses when some observations on the response variable are missing

The present article deals with the problem of estimation of parameters in a linear regression model when some data on response variable is missing and the responses are equi-correlated. The ordinary least squares and optimal homogeneous predictors are employed to find the imputed values of missing observations. Their efficiency properties are analyzed using the small disturbances asymptotic theory. The estimation of regression coefficients using these imputed values is also considered and a comparison of estimators is presented.     

Homosexual not Homogeneous: A Motivation-Based Typology of Gay Leisure Travelers Holidaying in Cape Town,South Africa

ABSTRACT

The gay traveler, a segment of the lesbian, gay, bisexual, and transgender (LGBT) travel market, is perceived to be a homogeneous market segment as a result of the assumption that gay men have a unique “homosexual lifestyle.” This assumption is problematic as it conceals many other important variables, and it may hinder effective destination marketing. A Web-based electronic survey was completed by 469 gay male travelers, and attribute-based benefit segmentation was carried out by applying a hierarchical cluster analysis using Ward’s procedure with Euclidean distances. The typology is based on the push and pull framework; the motivations of travelers were assessed both in terms of their socio-psychological motivations and destination attributes of Cape Town. The typology empirically suggests that gay travelers are not homogeneous, and that sexuality influences the travel behavior of a minority of gay men. Consequently, not all gay travelers or activities by these travelers can be labeled as “gay” tourism.     

联络及其在现代物理中的解释

本文从曲线坐标、曲面上向量平移入手,导入了联络,继而引入协变微分、短程线及曲率张量,最后指明联络在广义相对论中的意义。     

关于平面Clipping随机场的若干性质

讨论了平面随机场关于水平 μ 的 clipping 随机场的各向同性,齐性,独立性和可分性的若干结果.     

一类极坐标形式的自治系统的首次积分

研究了一类极坐标形式的Hamiltonian自治系统,采用积分因子的方法,在一定条件下获得了该系统的首次积分的一般形式,并举例说明该结论的应用.     

The importance of context specificity in work stress research: A test of the Demand-Control-Support model in academics

The Demand-Control (D-C) (Karasek, 1979) and the Demand-Control-Support (D-C-S) (Johnson & Hall, 1988; Johnson, Hall, & Theorell, 1989; Karasek & Theorell, 1990) models of work stress suggest that jobs with high demands and low control (and low support) are stressful. In line with the support in the literature for context-specificity in occupational stress research (Sparks & Cooper, 1999) and the limited and even contradictory support for interaction effects, the main aim of the present study was to examine how the D-C-S model applied in a well-defined occupational group. Using hierarchical regression analyses, and controlling for negative affect, the D-C-S model accounted for 26%, 6%, and 8% of the variance in job satisfaction, psychological distress and burnout, respectively, among 166 academics in a UK university. No two-way or three-way interactive effects were evident, but additive effects of job demands and control on psychological well-being and of job demands and support on both burnout and job satisfaction were shown, corroborating research showing that high job strain is linked to ill health and job dissatisfaction in this homogenous occupational sample. It is recommended that, in future, research includes more variables that are specific to a particular occupation.     

Some Estimates for the Rate of Convergence in the Multidimensional Central Limit Theorem for Homogeneous Markov Chains

This paper obtains some estimates for the rate of convergence in the multi-dimensional central limit theorem for vector-valued functions of a homogeneous Markov chain without assuming the finiteness of their absolute third moment. These estimates have a universal character and generalize the results that hold when the third moments are finite.     

Minimum,maximum, and average spherical prediction variances for central composite and box-behnken designs

Single value design optimality criteria are often considered when selecting a response surface design. An alternative to a single value criterion is to evaluate prediction variance properties throughout the experimental region and to graphically display the results in a variance dispersion graph (VDG) (Giovannitti-Jensen and Myers (1989)). Three properties of interest are the spherical average, maximum, and minimum prediction variances. Currently, a computer-intensive optimization algorithm is utilized to evaluate these prediction variance properties. It will be shown that the average, maximum, and minimum spherical prediction variances for central composite designs and Box-Behnken designs can be derived analytically. These three prediction variances can be expressed as functions of the radius and the design parameters. These functions provide exact spherical prediction variance values eliminating the implementation of extensive computing involving algorithms which do not guarantee convergence. This research is concerned with the theoretical development of these analytical forms. Results are presented for hyperspherical and hypercuboidal regions.     

Maximum Likelihood Estimation for Multinomial‐Poisson Models: A Generalization of Birch's Numerical Invariance Results

Abstract. This study gives a generalization of Birch's log‐linear model numerical invariance result. The generalization is given in the form of a sufficient condition for numerical invariance that is simple to verify in practice and is applicable for a much broader class of models than log‐linear models. Unlike Birch's log‐linear result, the generalization herein does not rely on any relationship between sufficient statistics and maximum likelihood estimates. Indeed the generalization does not rely on the existence of a reduced set of sufficient statistics. Instead, the concept of homogeneity takes centre stage. Several examples illustrate the utility of non‐log‐linear models, the invariance (and non‐invariance) of fitted values, and the invariance (and non‐invariance) of certain approximating distributions.     

Comparison of Kernel Discriminant Coordinates Algorithms

The construction of kernel discriminant coordinates reduces to the solution of a generalized eigenvalue problem in which both matrices are nonnegative definite. Six different algorithms for solving that problem are described, and the performance of these algorithms is tested on 26 different datasets. The percentage of misclassifications using a linear discriminant function is noted, and the algorithms’ running times are ascertained. Classification is also performed in the space of classical discriminant coordinates.