Estimation and comparison of lognormal parameters in the presence of censored data

It is assumed that k(k?>?2) independent samples of sizes n _i (i?=?1, …, k) are available from k lognormal distributions. Four hypothesis cases (H ₁ – H ₄) are defined. Under H ₁, all k median parameters as well as all k skewness parameters are equal; under H ₂, all k skewness parameters are equal but not all k median parameters are equal; under H ₃, all k median parameters are equal but not all k skewness parameters are equal; under H ₄, neither the k median parameters nor the k skewness parameters are equal. The Expectation Maximization (EM) algorithm is used to obtain the maximum likelihood (ML) estimates of the lognormal parameters in each of these four hypothesis cases. A (2k???1) degree polynomial is solved at each step of the EM algorithm for the H ₃ case. A two-stage procedure for testing the equality of the medians either under skewness homogeneity or under skewness heterogeneity is also proposed and discussed. A simulation study was performed for the case k?=?3.     

中国经济TFP增长的影响因素——基于BACE方法的综合分析

生产率增长是宏观经济研究的一个重要议题。采用经典估计贝叶斯平均(BACE)方法,引入模型的不确定性,在上万次回归的基础上,对众多影响生产率的解释变量按其重要性进行分类和排序。研究发现:在事先列出的16个可能的解释变量中,FDI进入程度、进口依存度、出口依存度、专科以上学历人数占从业人数的比率、相邻地区的生产率(空间邻居变量)以及市场化程度对TFP增长的解释能力最强,并且都具有良好的稳健性。FDI金额、进口额、出口额以及前一期研发投入等四个变量对TFP增长也有一定的解释能力。但各变量在我国东、中、西部地区的影响力存在显著差异。     

具有分子弥散的地下水水质污染模型的混合元──特征有限元方法

本文研究地下水水质污染理论中的一类非线性双曲一抛物耦合方程组的混合元一特征有限元方法，给出两类椭圆投影及其误差估计，构造出混合元－特征有限元全离散格式，证明了格式在Ｈ１模度量意义下的最优阶估计。     

Improved inference for a linear mixed-effects model when the subpopulation effects are clustered

We provide Bayesian methodology to relax the assumption that all subpopulation effects in a linear mixed-effects model have, after adjustment for covariates, a common mean. We expand the model specification by assuming that the m subpopulation effects are allowed to cluster into d groups where the value of d, 1?d?m, and the composition of the d groups are unknown, a priori. Specifically, for each partition of the m effects into d groups we only assume that the subpopulation effects in each group are exchangeable and are independent across the groups. We show that failure to take account of this clustering, as with the customary method, will lead to serious errors in inference about the variances and subpopulation effects, but the proposed, expanded, model leads to appropriate inferences. The efficacy of the proposed method is evaluated by contrasting it with both the customary method and use of a Dirichlet process prior. We use data from small area estimation to illustrate our method.     

重庆市农民工市民化成本模型构建及测算

重庆市城乡二元结构明显,农民工问题突出.农民工市民化问题具有十分重要的意义.文章结合相关研究成果对农民工市民化的私人成本和公共成本进行测算,通过分析认为应当加快构建政府、企业、社会、个人“四位一体”的成本分担机制并提出一些建议.     

On boosting kernel density methods for multivariate data: density estimation and classification

Statistical learning is emerging as a promising field where a number of algorithms from machine learning are interpreted as statistical methods and vice-versa. Due to good practical performance, boosting is one of the most studied machine learning techniques. We propose algorithms for multivariate density estimation and classification. They are generated by using the traditional kernel techniques as weak learners in boosting algorithms. Our algorithms take the form of multistep estimators, whose first step is a standard kernel method. Some strategies for bandwidth selection are also discussed with regard both to the standard kernel density classification problem, and to our 'boosted' kernel methods. Extensive experiments, using real and simulated data, show an encouraging practical relevance of the findings. Standard kernel methods are often outperformed by the first boosting iterations and in correspondence of several bandwidth values. In addition, the practical effectiveness of our classification algorithm is confirmed by a comparative study on two real datasets, the competitors being trees including AdaBoosting with trees.     

Linear Models in a general parametric form

The linear model Y - N(Xb, σ²∑) with a restriction R'b = M'u + c is considered, where X, R, M, ∑ and c are known. Explicit formulae are obtained for the best linear unbiased estimator of K'b, for the F-test of the hypothesis K'b = W'v + a, and for the simultaneous confidence intervals of the parameters K′_i b' s, where K = [K₁,K₂,…K_s], w, and a are known, none of the matrices X, ∑, R, M, K, and W is required to have full ranks, and the design X can be one - or multi-way,complete or incomplete, balanced or not balanced, connected or disconnected.     

Estimation in long memory time series models

A regression type estimator of the parameter d in fractionally differenced ARMA (p,q) processes is presented. The proposed estimator is shown to be mean square consistent. Its performance is compared with some of the existing estimators via a simulation study.     

Improved Confidence Statements for the Binomial Parameter

In the computation of two-sided confidence intervals for the binomial parameter p (using the binomial mass function), it is known that such intervals achieve a confidence coefficient that in general is not equal to the confidence level 1 – α, say. In this article we present some general results on the confidence coefficient and tabulate them for selected pairs (α, n = number of trials). We treat only the nominal equal tail probability case because it is the most commonly taught and used.     

Analysis of bivariate dichotomous data from a stratified two-stage cluster sample

In the health and social sciences, researchers often encounter categorical data for which complexities come from a nested hierarchy and/or cross-classification for the sampling structure. A common feature of these studies is a non-standard data structure with repeated measurements which may have some degree of clustering. In this paper, methodology is presented for the joint estimation of quantities of interest in the context of a stratified two-stage sample with bivariate dichotomous data. These quantities are the mean value π of an observed dichotomous response for a certain condition or time-point and a set of correlation coefficients for intra-cluster association for each condition or time period and for inter-condition correlation within and among clusters. The methodology uses the cluster means and pairwise joint probability parameters from each cluster. They together provide appropriate information across clusters for the estimation of the correlation coefficients.