Exponentiated half logistic distribution: Different estimation methods and joint confidence regions

In this article, we study the problem of estimating the unknown shape and scale parameters of the exponentiated half logistic distribution. For the maximum-likelihood estimation, we obtain a necessary and sufficient condition for the existence and uniqueness of maximum-likelihood estimates of the parameters. Inverse moment and modified inverse moment estimators are derived. Monte Carlo simulations are conducted to compare their performances. Two methods for constructing joint confidence regions for the two parameters are also proposed and their performances are discussed. A numerical example is presented to illustrate the methods.     

结构型一般EV线性模型参数估计的相合性研究

本文对结构型一般EV线性模型参数的研究,利用矩方法,得到了未知参数的强相合性。     

Strong consistency of the regularized least-squares estimates of infinite autoregressive models

Our main interest is on-line parameter estimation of infinite AR models with exponentially decaying coefficients. The practical importance of the problem follows from the fact that the class of such models includes (but not limited to) all causal invertible ARMA(p,q$p,q$) models. On-line parameter estimation means that the length of the observed data sample is not known a priori and may indefinitely increase. Hence, the parameter estimates should be refined upon arrival of every new observation. So use of the maximum likelihood (ML) method is not feasible due to the high computational burden, and recursive estimation procedures are preferable.     

Combining estimates from multiple early studies to obtain estimates of response: using shrinkage estimates to obtain estimates of response

Development of anti-cancer therapies usually involve small to moderate size studies to provide initial estimates of response rates before initiating larger studies to better quantify response. These early trials often each contain a single tumor type, possibly using other stratification factors. Response rate for a given tumor type is routinely reported as the percentage of patients meeting a clinical criteria (e.g. tumor shrinkage), without any regard to response in the other studies. These estimates (called maximum likelihood estimates or MLEs) on average approximate the true value, but have variances that are usually large, especially for small to moderate size studies. The approach presented here is offered as a way to improve overall estimation of response rates when several small trials are considered by reducing the total uncertainty.The shrinkage estimators considered here (James-Stein/empirical Bayes and hierarchical Bayes) are alternatives that use information from all studies to provide potentially better estimates for each study. While these estimates introduce a small bias, they have a considerably smaller variance, and thus tend to be better in terms of total mean squared error. These procedures provide a better view of drug performance in that group of tumor types as a whole, as opposed to estimating each response rate individually without consideration of the others. In technical terms, the vector of estimated response rates is nearer the vector of true values, on average, than the vector of the usual unbiased MLEs applied to such trials.     

A mixture model for dimension reduction

The existence of a dimension reduction (DR) subspace is a common assumption in regression analysis when dealing with high-dimensional predictors. The estimation of such a DR subspace has received considerable attention in the past few years, the most popular method being undoubtedly the sliced inverse regression. In this paper, we propose a new estimation procedure of the DR subspace by assuming that the joint distribution of the predictor and the response variables is a finite mixture of distributions. The new method is compared through a simulation study to some classical methods.     

Estimation for the parameters of the generalized half-normal distribution

As an applicable and flexible lifetime model, the two-parameter generalized half-normal (GHN) distribution has been received wide attention in the field of reliability analysis and lifetime study. In this paper maximum likelihood estimates of the model parameters are discussed and we also proposed corresponding bias-corrected estimates. Unweighted and weighted least squares estimates for the parameters of the GHN distribution are also presented for comparison purpose. Moreover, the likelihood ratio test is provided as complementary. Simulation study and illustrative examples are provided to compare the performance of the proposed methods.     

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。     

Estimating the Prevalence of Hard-to-Reach Populations: The Illustration of Mark-Recapture Methods in the Study of Homelessness

This paper assesses the potential of mark-recapture methods as a relatively powerful innovative research method for estimating the prevalence of "hard-to-reach"human populations in the social welfare field. We outline the development of mark-recapture methods, illustrating some recent applications. The body of the paper reviews the main methodological and practical questions raised by the method. We suggest, by way of illustration, how it might be applied to estimating the prevalence of rough sleepers in a given geographical region.     

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。     

A robust alternative to the normal distribution

A wider-tailed family of distributions is suggested as an alternative to the normal distribution having many of the desirable properties of the normal family. One advantage of this alternative is the greater robustness of maximum-likelihood estimates.