Nonresponse weighting adjustment using estimated response probability

To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator.     

Goodness‐of‐fit methods for matched case‐control studies

The authors propose graphical and numerical methods for checking the adequacy of the logistic regression model for matched case‐control data. Their approach is based on the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a centered Gaussian limit whose distribution can be approximated via computer simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a certain number of simulated realizations of the approximate limiting process under the null hypothesis. The proposed techniques allow one to check the functional form of each covariate, the logistic link function as well as the overall model adequacy. The authors assess the performance of the proposed methods through simulation studies and illustrate them using data from a cardiovascular study.     

Performance measurement and classification data in DEA: Input-oriented model

This paper presents an improved efficiency measurement tool by modifying the existing data envelopment analysis methodology to permit the incorporation of expert knowledge. A previous paper examined the inclusion of such knowledge within the additive model. This information appeared in the form of a binary classification of a subset of the decision making units under study (e.g. good versus poor performers). In the current paper, we extend this logic to the input-oriented radial projection model. We demonstrate that the inclusion of this and other forms of expert judgment can improve the performance of the DEA tool in the sense that the efficiency scores are more in line with expert/management beliefs.     

PATTERN RECOGNITION VIA ROBUST SMOOTHING WITH APPLICATION TO LASER DATA

Nowadays airborne laser scanning is used in many territorial studies, providing point data which may contain strong discontinuities. Motivated by the need to interpolate such data and preserve their edges, this paper considers robust nonparametric smoothers. These estimators, when implemented with bounded loss functions, have suitable jump‐preserving properties. Iterative algorithms are developed here, and are equivalent to nonlinear M‐smoothers, but have the advantage of resembling the linear Kernel regression. The selection of their coefficients is carried out by combining cross‐validation and robust‐tuning techniques. Two real case studies and a simulation experiment confirm the validity of the method; in particular, the performance in building recognition is excellent.     

Asthma Patients' Activities and Air Pollution: A Semiparametric Censored Regression Analysis

Asthma patients' health status may be especially sensitive to some types of air pollution, but the evidence on this is mixed. We explore the effects of ground-level ozone on asthma patient's activities, breaking apart the usual aggregated category of leisure into indoor and outdoor activities, and differentiating those by whether the activities were active or inactive. Applying the semiparametric censored estimation method we demonstrate that even though the period over which activities were observed was relatively low in ozone levels, there is a significant impact of ozone on a few activities. The (non-ozone) economic and demographic variables in the model play significant roles in explaining the allocation of time among seven activities, suggesting the suitability of the approach for other household decision-making contexts.     

Block external bootstrap in partially linear models with nonstationary strong mixing error terms

The authors extend the block external bootstrap to partially linear regression models with strongly mixing, nonstationary error terms. In addition to providing an approximate distribution for the semiparametric least square estimator of the parametric component, they propose a consistent estimator of the co‐variance matrix of this estimator.     

审美回归与英雄失色——兼论《金瓶梅》中的“水浒”人物

明代长篇小说在经历了神异化、传奇化、历史化的过程以后,作家们的目光转向了世俗和人间。这一转变,使小说作家的文学定位由以前的仰视天空或回首过去转向了对现实、对人生的描写。而那些叱咤于神话、历史和传奇故事中的英雄豪侠,诸如《水浒传》中的宋江、武松等人到了《金瓶梅》等一类世情作品中,便褪去了神奇的色彩,而被赋予了常人的属性,这是作家审美视角转换而导致的人性回归。     

分位数回归及应用简介

文章介绍了分位数回归法的概念、算法及主流统计软件R和SAS计算时的语法,并通过实例与以普通最小二乘法为基础的线性回归进行了对比,展现了分位数回归的巨大魅力。     

Semiparametric linear transformation models for current status data

Current status data arise when the death of every subject in a study cannot be determined precisely, but is known only to have occurred before or after a random monitoring time. The authors discuss the analysis of such data under semiparametric linear transformation models for which they propose a general inference procedure based on estimating functions. They determine the properties of the estimates they propose for the regression parameters of the model and illustrate their technique using tumorigenicity data.     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.