Testing for Persistence in the Error Component Model: A One-Sided Approach

This article proposes new simple testing procedures for the joint null hypothesis of absence of persistent effects, in the form of random effects and first-order serial correlation in the error component model. The fact that the presence of random effects is clearly of a one-sided nature, together with the fact that in many empirical applications researchers worry about positive serial correlation leaves room for a power gain that arises from restricting the parameter space under the alternative hypothesis, compared to existing procedures that allow for two-sided alternatives. A Monte Carlo experiment shows that the proposed statistics have good size and power performance in very small samples like those typically used in applied work in panel data. An empirical example illustrates the usefulness of the proposed statistics.     

Testing Serial Correlation in Partially Linear Single-Index Errors-in-Variables Models

In this article, we propose two test statistics for testing the underlying serial correlation in a partially linear single-index model Y = η(Z ^τα) + X ^τβ + ? when X is measured with additive error. The proposed test statistics are shown to have asymptotic normal or chi-squared distributions under the null hypothesis of no serial correlation. Monte Carlo experiments are also conducted to illustrate the finite sample performance of the proposed test statistics. The simulation results confirm that these statistics perform satisfactorily in both estimated sizes and powers.     

Preliminary Test Regression Estimator When two Prior Values of Population Mean (μx) of an Auxiliary Variable (x) are Available

A regression estimator using two prior values of population mean (μ_x) of an auxiliary variable (x) is proposed after a preliminary test of closeness of these prior values to the true valueμ_x. The proposed preliminary test regression estimator has been found to be more efficient in general than the usual regression estimator when prior values are used in place of μ_xwithout preliminary test of significance. The efficiency of the proposed estimator over the usual regression estimator has also been computed for different values of Δ₀, Δ₁, n, and ρ, which showed considerable gain in precision.     

A Comparison of Some Tests for Determining the Number of Nonzero Canonical Correlations

When considering the relationships between two sets of variates, the number of nonzero population canonical correlations may be called the dimensionality. In the literature, several tests for dimensionality in the canonical correlation analysis are known. A comparison of seven sequential test procedures is presented, using results from some simulation study. The tests are compared with regard to the relative frequencies of underestimation, correct estimation, and overestimation of the true dimensionality. Some conclusions from the simulation results are drawn.     

Simultaneous Tests for Independence Among Components of Random Vector by Step-Down Multiple Comparison Procedure

In this article, we consider testing independence among components of random vector in multivariate normal population. For testing independence, we use the modified likelihood ratio test statistic which is improved an approximation to χ² distribution of the likelihood ratio test statistic. In order to perform simultaneous tests for independence among components of random vector, we use the step-down multiple comparison procedure based on the closed testing procedure proposed by Marcus et al. (1976 Marcus , R. , Peritz , E. , Gabriel , K. R. ( 1976 ). On closed testing procedures with special reference to ordered analysis of variance . Biometrika 63 : 655 – 660 .[Crossref], [Web of Science ®] , [Google Scholar]). Finally, we perform Monte Carlo simulations and present numerical results.     

Rényi statistics for testing equality of autocorrelation coefficients

The problem of testing for equality of autocorrelation coefficients of two populations in multivariate data when errors are autocorrelated is considered. We derive Rényi statistics defined as divergences between unrestricted and restricted estimated joint probability density functions and we show that they are asymptotically chi-square distributed under the null hypothesis of interest. Monte Carlo simulation experiments are carried out to investigate the behavior of Rényi statistics and to make comparisons with test statistics based on the approach of Bhandary [M. Bhandary, Test for equality of autocorrelation coefficients for two populations in multivariate data when the errors are autocorrelated, Statistics & Probability Letters 73 (2005) 333–342] for the problem under consideration. Rényi statistics showed to have significantly better behavior.     

New approaches to model-free dimension reduction for bivariate regression

Dimension reduction with bivariate responses, especially a mix of a continuous and categorical responses, can be of special interest. One immediate application is to regressions with censoring. In this paper, we propose two novel methods to reduce the dimension of the covariates of a bivariate regression via a model-free approach. Both methods enjoy a simple asymptotic chi-squared distribution for testing the dimension of the regression, and also allow us to test the contributions of the covariates easily without pre-specifying a parametric model. The new methods outperform the current one both in simulations and in analysis of a real data. The well-known PBC data are used to illustrate the application of our method to censored regression.     

A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix

In this paper, a new test for the equality of the mean vectors between a two groups with the same number of the observations in high-dimensional data. The existing tests for this problem require a strong condition on the population covariance matrix. The proposed test in this paper does not require such conditions for it. This test will be obtained in a general model, that is, the data need not be normally distributed.     

基于类复向量原理的机器人位置和姿态分析

介绍了双实部类复向量的有关原理,建立了机器人位置的类复向量方程,并以6R机械手为例,论述机器人位置和姿态分析的类复向量方法。本文的方法与现行坐标变换矩阵法相比,变换次数和被变换的向量个数明显减少,从而使计算过程大大简化。