方差未知时两样本正态混合模型齐一性的修正似然比检验

本文讨论了方差未知时检验两样本正态混合模型齐一性的修正似然比统计量的极限性质,证明了原假设下修正似然比统计量的渐近分布为自由度为1的卡方分布.     

方差未知时两样本正态混合模型齐-性的修正似然比检验

本文讨论了方差未知时检验两样本正态混合模型齐一性的修正似然比统计量的极限性质,证明了原假设下修正似然比统计量的渐近分布为自由度为1的卡方分布.     

竞争风险混合模型的参数估计与检验

本文在独立同分布I型区间删失情形下,研究了竞争风险混合模型中当参数真值是内点时,参数极大似然估计的性质,获得了其强相合性和渐近正态性.在较为宽松的条件下,给出了竞争风险混合模型参数序关系假设检验的检验方法,同时得到了似然比检验统计量及其在零假设下的渐近分布为加权x~2分布,并给出了—个例子并进行了功效比较.     

多元正态分布的VDR条件拟合优度检验

提出多元正态性χ2检验统计量.多元正态分布转换样本Yd=RVd服从PearsonII型分布,证明了R2服从贝塔分布.基于贝塔分布和单位球均匀分布,得到多元正态性检验统计量χ2的渐近卡方分布.功效模拟显示,χ2统计量优于已有主要多元正态性检验统计量.做iris数据多元正态性的拟合优度检验.     

基于投影偏度和投影峰度的投影寻踪自助法的正态性检验

本文研究了多元分布的正态性检验问题,用投影寻踪自助法,获得了投影偏度和投影峰度正态性检验统计量,证明了在零假设成立时,所提出的偏度和峰度检验统计量的极限分布为一高斯过程的上界.为计算机模拟计算提供了有力的手段和依据.     

从偏态Pearson VII分布生成的新的多元偏态t分布

一般而言, 偏态的椭球等高分布是一类分布族,有相当一部分的分布都是积分形式, 且此类积分不易求出,而偏态的正态、偏态的正态尺度混合、偏态的PVII型、偏态的PII型的分布却有着很好的结构,偏态t分布属于偏态PVII型分布, 因此,本文在偏态PVII型分布的基础上着重研究新的偏态t分布,给出它的背景、定义、两种随机表示及其等价性.     

从偏态Pearson Ⅶ分布生成的新的多元偏态t分布

一般而言,偏态的椭球等高分布是一类分布族,有相当一部分的分布都是积分形式,且此类积分不易求出,而偏态的正态、偏态的正态尺度混合、偏态的PⅦ型、偏态的PⅡ型的分布却有着很好的结构,偏态t分布属于偏态PⅦ型分布,因此,本文在偏态PⅦ型分布的基础上着重研究新的偏态t分布,给出它的背景、定义、两种随机表示及其等价性.     

具部分缺失数据的两个柏松总体的估计和检验

本文讨论部分缺失数据两柏松分布总体的参数估计和总体相同的似然比检验,证明了估计的强相合性和渐近正态性,给出了似然比检验的极限分布,并讨论了基于精确分布的检验问题.     

含结构参数的二元正态混合模型齐一性的修正似然比检验

研究含一个未知结构参数的二元正态混合模型齐一性检验的修正似然比统计量的渐近性质, 证明了修正似然比统计量在零假设下的极限分布为χ²₂.     

具有部分缺失数据时两个Poisson总体的估计和检验

讨论了部分缺失数据两个 Poisson总体的参数估计和关于总体相同的似然比检验 ,证明了估计的强相合性和渐近正态性 ,指出了似然比检验统计量的极限分布 ,并讨论了基于精确分布的检验问题     

有限混合模型有限制Log极大似然比统计量的极限分布

有限混合模型的Log极大似然比统计量极限分布不是平常x2分布,1985年已为Hartigan指出.在这篇文章我们限制了混合比大于一正数下,讨论了两个含单个未知参数混合模型的Log极大似然比统计量的极限分布,它是零与x2分布的混合分布.     

The log-likelihood ratio for sparse multinomial mixtures

The log likelihood ratio is expanded for testing a sequence of multinomial null hypotheses against a sequence of multinomial mixture close alternative hypothesis. As the number of categories grows without limit, the sample size increases and the variances of the mixing distributions tend to zero. The limiting form of the log likelihood ratio is functionally different from previously studied goodness of fit statistics. The statistic derived here exhibits moderate asymptotic power when Pearson's chi-square is biased.     

Empirical likelihood for median regression model with designed censoring variables

We propose a new and simple estimating equation for the parameters in median regression models with designed censoring variables, and then apply the empirical log likelihood ratio statistic to construct confidence region for the parameters. The empirical log likelihood ratio statistic is shown to have a standard chi-square distribution, which makes this method easy to implement. At the same time, another empirical log likelihood ratio statistic is proposed based on an existing estimating equation and the limiting distribution of the empirical likelihood ratio statistic is shown to be a sum of weighted chi-square distributions. We compare the performance of the empirical likelihood confidence region based on the new estimating equation, with that based on the existing estimating equation and a normal approximation method by simulation studies.     

The Bahadur-Kiefer Representation of the Two Dimensional Spatial Medians

We consider the asymptotic behavior, both in distribution and almost sure, of the Bahadur-Kiefer representation of the two dimensional spatial medians. The rates appearing in this expansion are non-standard. The rate in the almost sure expansion is n(2 log n)-1/2(2 log log n)-1. The set of clusters points in the almost sure representation is obtained. The distribution of the Bahadur-Kiefer representation of the two dimensional spatial medians converges with rate n(2 log n)-1/2 to a limit that is determined precisely.     

Bayesian time series regression with nonparametric modeling of autocorrelation

Series models have several functions: comprehending the functional dependence of variable of interest on covariates, forecasting the dependent variable for future values of covariates and estimating variance disintegration, co-integration and steady-state relations. Although the regression function in a time series model has been extensively modeled both parametrically and nonparametrically, modeling of the error autocorrelation is mainly restricted to the parametric setup. A proper modeling of autocorrelation not only helps to reduce the bias in regression function estimate, but also enriches forecasting via a better forecast of the error term. In this article, we present a nonparametric modeling of autocorrelation function under a Bayesian framework. Moving into the frequency domain from the time domain, we introduce a Gaussian process prior to the log of the spectral density, which is then updated by using a Whittle approximation for the likelihood function (Whittle likelihood). The posterior computation is simplified due to the fact that Whittle likelihood is approximated by the likelihood of a normal mixture distribution with log-spectral density as a location shift parameter, where the mixture is of only five components with known means, variances, and mixture probabilities. The problem then becomes conjugate conditional on the mixture components, and a Gibbs sampler is used to initiate the unknown mixture components as latent variables. We present a simulation study for performance comparison, and apply our method to the two real data examples.     

Using mixture density functions for modelling of wage distributions

This article explores the possibility of modeling the wage distribution using a mixture of density functions. We deal with this issue for a long time and we build on our earlier work. Classical models use the probability distribution such as normal, lognormal, Pareto, etc., but the results are not very good in the last years. Changing the parameters of a probability density over time has led to a degradation of such models and it was necessary to choose a different probability distribution. We were using the idea of mixtures of distributions (instead of using one classical density) in previous articles. We tried using a mixture of probability distributions (normal, lognormal and a mixture of Johnson’s distribution densities) in our models. The achieved results were very good. We used data from Czech Statistical Office covering the wages of the last 18 years in Czech Republic.     

On approximation of the level probabilities for testing ordered parallel regression lines

Approximations for the level probabilities in testing order-restricted hypotheses are examined in this paper for generalized linear models with common slope. It is showed, in particular, that the asymptotic null distribution of the likelihood ratio statistic is a mixture of chi-squared distributions for the cases of simple order and simple tree order. Under a balanced structure, the asymptotic null distribution reduces to the well-known chi-bar-squared distribution. The use of the equal-weights level probabilities is also investigated. This approximation seems to be satisfactory when the sample sizes and the levels of the covariate are not too different among the strata.     

Optimizing information using the EM algorithm in item response theory

Latent trait models such as item response theory (IRT) hypothesize a functional relationship between an unobservable, or latent, variable and an observable outcome variable. In educational measurement, a discrete item response is usually the observable outcome variable, and the latent variable is associated with an examinee’s trait level (e.g., skill, proficiency). The link between the two variables is called an item response function. This function, defined by a set of item parameters, models the probability of observing a given item response, conditional on a specific trait level. Typically in a measurement setting, neither the item parameters nor the trait levels are known, and so must be estimated from the pattern of observed item responses. Although a maximum likelihood approach can be taken in estimating these parameters, it usually cannot be employed directly. Instead, a method of marginal maximum likelihood (MML) is utilized, via the expectation-maximization (EM) algorithm. Alternating between an expectation (E) step and a maximization (M) step, the EM algorithm assures that the marginal log likelihood function will not decrease after each EM cycle, and will converge to a local maximum. Interestingly, the negative of this marginal log likelihood function is equal to the relative entropy, or Kullback-Leibler divergence, between the conditional distribution of the latent variables given the observable variables and the joint likelihood of the latent and observable variables. With an unconstrained optimization for the M-step proposed here, the EM algorithm as minimization of Kullback-Leibler divergence admits the convergence results due to Csiszár and Tusnády (Statistics & Decisions, 1:205–237, 1984), a consequence of the binomial likelihood common to latent trait models with dichotomous response variables. For this unconstrained optimization, the EM algorithm converges to a global maximum of the marginal log likelihood function, yielding an information bound that permits a fixed point of reference against which models may be tested. A likelihood ratio test between marginal log likelihood functions obtained through constrained and unconstrained M-steps is provided as a means for testing models against this bound. Empirical examples demonstrate the approach.