Robust Testing Procedures in Heteroscedastic Linear Models

There exist many studies which treat the robust tests in homoscedastic linear models. However, the robust testing procedure in heteroscedastic linear models has not been examined. In this article, three classes of testing procedures for testing subhypothesis in heteroscedastic linear models are developed. These are Wald-type, score-type, and drop-in dispersion tests. The asymptotic distributions of these tests are obtained under the null hypothesis and contiguous alternatives. For a robustness criterion, the maximum asymptotic bias of the level of the test for distributions in a shrinking contamination neighborhood is used and the most-efficient robust test is derived. Finally, the performance of these tests in small sample is studied by Monte Carlo simulation.     

New Algorithms for Evaluating the Log-Likelihood Function Derivatives in the AI-REML Method

In this study, we propose several improvements of the Average Information Restricted Maximum Likelihood algorithms for estimating the variance components for genetic mapping of quantitative traits. The improved methods are applicable when two variance components are to be estimated. The improvements are related to the algebraic part of the methods and utilize the properties of the underlying matrix structures.

In contrast to previously developed algorithms, the explicit computation of a matrix inverse is replaced by the solution of a linear system of equations with multiple right-hand sides, based on a particular matrix decomposition. The computational costs of the proposed algorithms are analyzed and compared.     

Comment on Ridge Estimation to the Restricted Linear Model

In this note, we show that the estimator and the following results given by Zhong and Yang (2007 Zhong , Z. , Yang , H. ( 2007 ). Ridge estimation to the restricted linear model . Commun. Statist. Theor. Meth. 36 : 2099 – 2115 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) are the same with that of Groß (2003 Groß , J. ( 2003 ). Restricted ridge estimation . Statist. Probab. Lett. 65 : 57 – 64 .[Crossref], [Web of Science ®] , [Google Scholar]).     

Analytical Characterization of the Information Matrix for Split-Plot Designs

This article provides the analytical characterization of the inverse of the information matrix for second-order SPD. A particular feature of these explicit expressions is that they are functions of the design parameters enabling the development of analytical functions to efficiently compute exact design optimality criteria. The application of these analytical expressions is demonstrated using the generalized variance of the parameter estimates for second-order SPD. An example illustrating the use of these expressions is also presented.     

Local Influence Under Parameter Constraints

Calculations of local influence curvatures and leverage have been well developed when the parameters are unrestricted. In this article, we discuss the assessment of local influence and leverage under linear equality parameter constraints with extensions to inequality constraints. Using a penalized quadratic function we express the normal curvature of local influence for arbitrary perturbation schemes and the generalized leverage matrix in interpretable forms, which depend on restricted and unrestricted components. The results are quite general and can be applied in various statistical models. In particular, we derive the normal curvature under three useful perturbation schemes for generalized linear models. Four illustrative examples are analyzed by the methodology developed in the article.     

Highly Efficient Designs for Logistic Models with Categorical Variables

Optimal designs for logistic models generally require prior information about the values of the regression parameters. However, experimenters usually do not have full knowledge of these parameters. We propose a design that is D-optimal on a restricted design region. This design assigns an equal weight to design points that contain more information and ignores those design points that contain less information about the regression parameters. The design can be constructed in practice by means of the rank order of the outcome variances. A numerical study compares the proposed design with the D-optimal and completely balanced designs in terms of efficiency.     

Restricted Ridge Estimators of the Parameters in Semiparametric Regression Model

In this article, we introduce a ridge estimator for the vector of parameters β in a semiparametric model when additional linear restrictions on the parameter vector are assumed to hold. We also obtain the semiparametric restricted ridge estimator for the parametric component in the semiparametric regression model. The ideas in this article are illustrated with a data set consisting of housing prices and through a comparison of the performances of the proposed and related estimators via a Monte Carlo simulation.     

Two Kinds of Restricted Estimators in Singular Linear Regression

In this article, the parameter estimators in singular linear model with linear equality restrictions are considered. The restricted root estimator and the generalized restricted root estimator are proposed and some properties of the estimators are also studied. Furthermore, we compare them with the restricted unified least squares estimator and show their sufficient conditions under which their superior over the restricted unified least squares estimator in terms of mean squares error, and discuss the choice of the unknown parameters of the generalized restricted root estimator.     

关于多项选择题的讨论──外语教育语言学研究之四

多项选择题是现代外语测试中运用得最多的题型之一。本文就这种题型产生的背景及其构成做了综合性的阐述；并对设计这种试题应注意的一些问题进行了探讨。此外，本文还针对多项选择题在外语教学中的应用问题提出了一些看法和建议。     

Families Home Schooling in a Virtual Charter School System

No abstract available for this article.