M-estimation and model identification based on double SCAD penalization

M-estimation is a widely used method for robust statistical inference. In this article, using a B-spline series approximation with a double smoothly clipped absolute deviation penalization, we solve the problem of simultaneous variable selection and parametric component identification in a non parametric additive model. The theoretical properties of the double non concave penalized M-estimation are established. The proposed approach is resistant to heavy-tailed errors or outliers in the responses. Simulation studies for finite-sample cases are conducted and a real dataset is also analyzed for illustration of this new approach.     

基于非径向BML-DEA模型的地区工业环境绩效测度

本文结合BM方向距离函数、非径向DEA模型以及Luenberger生产率指标的特点,发展了一种非径向BML-DEA模型测度环境绩效,并对中国30个省市1997—2011年地区工业环境绩效变化趋势进行了分析。研究发现:整体上中国地区工业环境绩效增长主要来源于技术进步而非效率改善;中国最发达的4个省市——北京、上海、江苏和广东的工业环境绩效的年均增长幅度最大;3种污染物处理对工业环境绩效增长的贡献较为均衡;不同地区的工业环境绩效及其分解成分存在显著差异,东部地区的工业环境绩效增长速度显著高于其他地区,但效率增长速度略低于中部地区。因此,各地区提升资源的有效利用水平迫在眉睫。     

Efficient Estimation for Semi-varying Coefficient Model with An Invertible Linear Process Error

For semi-varying-coefficient model with an invertible linear process error, we propose an efficient estimator procedure. This procedure is based on a pre-whitening transformation of the dependent variable that must be estimated from the data. We establish the proposed estimations’ asymptotic normalities, and assess their finite sample performance. Monte Carlo simulation suggest that the efficiency gain can be achieved in moderate-sized samples.     

Matrix Euclidean norm Wielandt inequalities and their applications to statistics

In this paper we shall establish a new matrix inequality which will fill the gap that there has not been any matrix Euclidean norm version of the Wielandt inequality in the literature yet. This inequality can be used to present an upper bound of a new measure of association which plays a very important role in statistics, especially in multivariate analysis. A new alternative based on Euclidean norm for relative gain of the covariance adjusted estimator of parameters is provided.     

The estimating function bootstrap

The authors propose a bootstrap procedure which estimates the distribution of an estimating function by resampling its terms using bootstrap techniques. Studentized versions of this so‐called estimating function (EF) bootstrap yield methods which are invariant under reparametrizations. This approach often has substantial advantage, both in computation and accuracy, over more traditional bootstrap methods and it applies to a wide class of practical problems where the data are independent but not necessarily identically distributed. The methods allow for simultaneous estimation of vector parameters and their components. The authors use simulations to compare the EF bootstrap with competing methods in several examples including the common means problem and nonlinear regression. They also prove symptotic results showing that the studentized EF bootstrap yields higher order approximations for the whole vector parameter in a wide class of problems.     

Two kinds of restricted modified estimators in linear regression model

In this paper, we introduce two kinds of new restricted estimators called restricted modified Liu estimator and restricted modified ridge estimator based on prior information for the vector of parameters in a linear regression model with linear restrictions. Furthermore, the performance of the proposed estimators in mean squares error matrix sense is derived and compared. Finally, a numerical example and a Monte Carlo simulation are given to illustrate some of the theoretical results.     

中国省份消费水平指数的波动源头研究

文章通过构建基于序贯主成分方法估算的状态空间分层动态因子模型,对我国1999年1月到2015年6月31个省份的CPI即消费水平指标的三层动态因子进行估算.将CPI波动来源分解为相互独立的国家、区域和省份动态因子,进而分析其基本特征,结果表明全国动态因子和区域动态因子基本反映了各省份消费水平的波动趋势,可作为CPI波动的主要源头,然而省份动态因子差异较大,其差异性与各省份经济发展水平相关.并利用方差分解来解释CPI波动源头,其中,全国动态因子是CPI波动的主要驱动力,区域动态因子影响力最弱,省份动态因子的影响程度差异较大,在一定程度上与各区域及各省份的特殊特征有关.     

Confirmatory analysis for phase III population pharmacokinetics

Population pharmacokinetics (POPPK) has many important uses at various stages of drug development and approval. At the phase III stage, one of the major uses of POPPK is to identify covariate influences on human pharmacokinetics, which is important for potential dose adjustment and drug labeling. One common analysis approach is nonlinear mixed‐effect modeling, which typically involves time‐consuming extensive search for best fits among a large number of possible models. We propose that the analysis goal can be better achieved with a more standard confirmatory statistical analysis approach, which uses a prespecified primary analysis and additional sensitivity analyses. We illustrate this approach using a phase III study data set and compare the result with that calculated using the common exploratory approach. We argue that the confirmatory approach not only substantially reduces analysis time but also yields more accurate and interpretable results. Some aspects of this confirmatory approach may also be extended to data analysis in earlier stages of clinical drug development, i.e. phase II and phase I. Copyright © 2009 John Wiley & Sons, Ltd.     

Restricted Estimation in Partially Linear Errors-in-Variables Models

As a compromise between parametric regression and nonparametric regression, partially linear models are frequently used in statistical modelling. This article considers statistical inference for this semiparametric model when the linear covariate is measured with additive error and some additional linear restrictions on the parametric component are assumed to hold. We propose a restricted corrected profile least-squares estimator for the parametric component, and study the asymptotic normality of the estimator. To test hypothesis on the parametric component, we construct a Wald test statistic and obtain its limiting distribution. Some simulation studies are conducted to illustrate our approaches.     

A simple algorithm for the adaptation of scores and power behavior of the corresponding rank test

The purpose of this paper is twofold:On one hand we want to give a very simple algorithm for evaluating a special rank estimator of the type given in Behnen, Neuhaus, and Ruymgaart (1983) for the approximate optimal choice of the scores-generating function of a two-sample linear rank test for the general testing problem H_o:F=G versus H₁:F ≤ G, F ≠ G, in order to demonstrate that the corresponding adaptive rank statistic is simple enough for practical applications. On the other hand we prove the asymptotic normality of the adaptive rank statistic under H (leading to approximate critical values) and demonstrate the adaptive behavior of the corresponding rank test by a Monte Carlo power simulation for sample sizes as low as m=10, n=10.