Testing equality of two normal covariance matrices with monotone missing data

Abstract

The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.     

A Graphical Tool for Interpreting Regression Coefficients of Trinomial Logit Models

Multinomial logit (also termed multi-logit) models permit the analysis of the statistical relation between a categorical response variable and a set of explicative variables (called covariates or regressors). Although multinomial logit is widely used in both the social and economic sciences, the interpretation of regression coefficients may be tricky, as the effect of covariates on the probability distribution of the response variable is nonconstant and difficult to quantify. The ternary plots illustrated in this article aim at facilitating the interpretation of regression coefficients and permit the effect of covariates (either singularly or jointly considered) on the probability distribution of the dependent variable to be quantified. Ternary plots can be drawn both for ordered and for unordered categorical dependent variables, when the number of possible outcomes equals three (trinomial response variable); these plots allow not only to represent the covariate effects over the whole parameter space of the dependent variable but also to compare the covariate effects of any given individual profile. The method is illustrated and discussed through analysis of a dataset concerning the transition of master’s graduates of the University of Trento (Italy) from university to employment.     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

双波长比值法测定水杨酸、间苯二酚、对氨基苯甲酸的含量

依据水杨酸、间苯二酚、对氨基苯甲酸三组分的比值光谱特征,以对氨基苯甲酸为干扰组分,选择241nm、278nm作为测定水杨酸的波长;以水杨酸作为干扰组分,选择214nm、240nm作为测定间苯二酚的波长;以间苯二酚作为干扰组分,选择222nm、279nm作为测定对氨基苯甲酸的波长.结果显示,水杨酸浓度在1~80mg/L,基本二酚浓度在0.8~40mg/L,对氨基苯甲酸浓度在0.4~40mg/L范围内具有良好的线性关系.本方法具有测定波长少,光谱分离能力强、计算简单、能在低档分光光度计上实现、易于推广等特点.     

Visualization of generalized mean estimators using auxiliary information in survey sampling

Abstract

The mean estimators with ratio depend on multiple auxiliary variables and unknown parameters in a finite population setting. We propose a new generalized approach with matrices for modeling the mutivariate mean estimators with two auxiliary variables. Our approach brings naturally a graphical analysis for comparing mean estimators.     

上证30指数股市盈率实证分析

通过对2000年上证30指数股市盈率水平及其影响因素进行实证分析,可以发现,其中股息支付率、每股收益增长率、行业平均市盈率对股票P/E值起主要的解释作用,而股本规模因素,解释力却有限,除每股收益增长率外,各变量的相关性与理论分析基本一致.     

一种新型的GSM接收机的设计与实现

依据GSM协议,结合GSM数字移动通信系统信号传输的特点,给出了基于GSM系统的带软输出结构的MLSE接收机的设计方案,阐述了MLSE接收机的实现算法。实际环境的试验结果表明:工程实现的MLSE接收机具有良好的工作性能和一定的实际应用价值。     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

钢纤维混凝土基本性能试验研究

通过对30个立方体试块抗压强度试验,分析了钢纤维对普通混凝土的增强作用;通过对30个小梁试件三分点加载抗折试验,研究了体积率对钢纤维混凝土(SFRC)荷载-挠度关系及荷载-极限应变关系的影响.在脆性基体中掺入钢纤维,有效地提高了复合材料承受荷载时阻止裂缝引发与扩展的能力,达到对混凝土的增强效果,尤其是对于抗折强度的增强作用,使混凝土的破坏形式由脆性破坏变为塑性破坏.