Coarsened Propensity Scores and Hybrid Estimators for Missing Data and Causal Inference

In the areas of missing data and causal inference, there is great interest in doubly robust (DR) estimators that involve both an outcome regression (RG) model and a propensity score (PS) model. These DR estimators are consistent and asymptotically normal if either model is correctly specified. Despite their theoretical appeal, the practical utility of DR estimators has been disputed (e.g. Kang and Schaffer, Statistical Science 2007; 22: 523–539). One of the major concerns is the possibility of erratic estimates resulting from near‐zero denominators due to extreme values of the estimated PS. In contrast, the usual RG estimator based on the RG model alone is efficient when the RG model is correct and generally more stable than the DR estimators, although it can be biased when the RG model is incorrect. In light of the unique advantages of the RG and DR estimators, we propose a class of hybrid estimators that attempt to strike a reasonable balance between the RG and DR estimators. These hybrid estimators are motivated by heuristic arguments that coarsened PS estimates are less likely to take extreme values and less sensitive to misspecification of the PS model than the original model‐based PS estimates. The proposed estimators are compared with existing estimators in simulation studies and illustrated with real data from a large observational study on obstetric labour progression and birth outcomes.     

Parametric Inference for Diffusion Processes Observed at Discrete Points in Time: a Survey

This paper is a survey of estimation techniques for stationary and ergodic diffusion processes observed at discrete points in time. The reader is introduced to the following techniques: (i) estimating functions with special emphasis on martingale estimating functions and so-called simple estimating functions; (ii) analytical and numerical approximations of the likelihood function which can in principle be made arbitrarily accurate; (iii) Bayesian analysis and MCMC methods; and (iv) indirect inference and EMM which both introduce auxiliary (but wrong) models and correct for the implied bias by simulation.     

A statistical model for evaluating the patient satisfaction

The paper aims to identify the drivers which affect the patient satisfaction. For this purpose the authors analyzed the data collected by delivering a questionnaire to patients hospitalizing in an important hospital of Napoli city.The effects of level of satisfaction about different services received in the hospital on the patient satisfaction have been studied by comparing the results of four regression models. Findings provide empirical evidence that a policy aimed at developing the competences and the abilities of the staff and at maintaining high standards of hygiene and cleanliness is the most appropriate strategy to improve patient satisfaction.     

基于Gabor小波和增强Fisher模型的掌纹特征提取

对于其他身份识别的生物特性而言掌纹有着很多的先天优势,因此在各个领域得到了非常广泛的应用。本文通过研究掌纹的特征,提出了一种基于Gabor小波和增强Fisher线性判别模型(EFM)的掌纹特征提取算法。先对预处理掌纹,在对掌纹灰色图像通过算法进行Gabor小波变换后,得到Gabor的掌纹特征向量。随后,通过主成分分析变换高维特征向量至低维空间,最后在此空间内利用EFM变换矩阵提取到掌纹的特征。由于Gabor函数在特征提取方面有着优良的性能,对高维特征的降维处理问题可有效解决,同时,算法也提高了Fisher线性判别式(FLD)的推广能力,可以较好地实现掌纹的特征提取。     

Subsampling Inference with K Populations and a Non‐standard Behrens–Fisher Problem

We revisit the methodology and historical development of subsampling, and then explore in detail its use in hypothesis testing, an area which has received surprisingly modest attention. In particular, the general set‐up of a possibly high‐dimensional parameter with data from K populations is explored. The role of centring the subsampling distribution is highlighted, and it is shown that hypothesis testing with a data‐centred subsampling distribution is more powerful. In addition we demonstrate subsampling’s ability to handle a non‐standard Behrens–Fisher problem, i.e., a comparison of the means of two or more populations which may possess not only different and possibly infinite variances, but may also possess different distributions. However, our formulation is general, permitting even functional data and/or statistics. Finally, we provide theory for K ‐ sample U ‐ statistics that helps establish the asymptotic validity of subsampling confidence intervals and tests in this very general setting.     

Estimating regression coefficients from survey data by asymptotic design-cum-model based approach

Postulating a linear regression of a variable of interest on an auxiliary variable with values of the latter known for all units of a survey population, we consider appropriate ways of choosing a sample and estimating the regression parameters. Recalling Thomsen’s (1978) results on non-existence of ‘design-cum-model’ based minimum variance unbiased estimators of regression coefficients we apply Brewer’s (1979) ‘asymptotic’ analysis to derive ‘asymptotic-design-cummodel’ based optimal estimators assuming large population and sample sizes. A variance estimation procedure is also proposed.     

Model assisted survey sampling strategy in two phases

Postulating a super-population regression model connecting a size variable, a cheaply measurable variable and an expensively observable variable of interest, an asymptotically optimal double sampling strategy to estimate the survey population total of the third variable is specified. To render it practicable, unknown model-parameters in the optimal estimator are replaced by appropriate statistics. The resulting generalized regression estimator is then shown to have a model-cum-asymptotic design based expected square error equal to that of the asymptotically optimum estimator itself. An estimator for design variance of the estimator is also proposed.     

Logistic Regression, a review

A review is given of the development of logistic regression as a multi-purpose statistical tool.
A historical introduction shows several lines culminating in the unifying paper of Cox (1966), in which theory as developed in the field of bio-assay is shown to be applicable to designs as discriminant-analysis and case-control study. A review is given of several designs all leading to the same analysis. The link is made with epidemiological literature.
Several optimization criteria are discussed that can be used in the case of more observations per cell, namely maximum likelihood, minimum chi-square and weighted regression on the observed logits. Recent literature on the goodness of fit problem is reviewed and finally, comments are made about the non-parametric approach to logistic regression which is still in rapid development.     

Rank tests for testing randomness of a regression coefficient in a linear regression model

We propose a class of nonparametric tests for testing non-stochasticity of the regression parameterβ in the regression modely _i =βx _i +ɛ _i ,i=1, ...,n. We prove that the test statistics are asymptotically normally distributed both underH ₀ and under contiguous alternatives. The asymptotic relative efficiencies (in the Pitman sense) with respect to the best parametric test have also been computed and they are quite high. Some simulation studies are carried out to illustrate the results. Research was supported by the University Grants Commission, India.     

Bayesian and Frequentist Inference for Ecological Inference: The R×C Case

In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R × C contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta hierarchical model developed by K ing , R osen and T anner (1999) from the 2×2 case to the R × C case. As in the 2×2 case, the inferential procedure employs Markov chain Monte Carlo (MCMC) methods. As such, the resulting MCMC analysis is rich but computationally intensive. The frequentist approach, based on first moments rather than on the entire likelihood, provides quick inference via nonlinear least-squares, while retaining good frequentist properties. The two approaches are illustrated with simulated data, as well as with real data on voting patterns in Weimar Germany. In the final section of the paper we provide an overview of a range of alternative inferential approaches which trade-off computational intensity for statistical efficiency.     

基于聚类分析和Fisher判别分析的军用车辆器材分级方法研究

军用车辆器材的分类、分级是器材质量评估的直观表述。针对现有形势下军用车辆器材中分级不明确,重点不突出且不易掌握的特点,运用聚类分析和Fisher判别分析方法进行研究,解决军用车辆器材的分级问题。通过对总部器材专家对某车型21种器材打分情况的分析,证明了以上方法在处理军用车辆器材分类、分级问题中的有效性。     

Classification of micro,small and medium enterprises (M-SME) based on their available levels of knowledge

This paper proposes a novel and practical method of classifying micro, small and medium enterprises (M-SME) based on their available levels of knowledge. This classification considers that the specific problems that dominate a company’s operations are related to the level of knowledge available. Therefore, identifying the dominant problem groups facing the company can enable an estimate of the level of knowledge available in the enterprise.Data from 2698 Mexican M-SMEs are used to identify operational problems. The main problem groups are obtained through a cluster analysis. The proposed classification consists of three levels of knowledge based on the interpretation of the dominant problem groups in each enterprise. Each of the 2698 Mexican M-SME are classified by a discriminant analysis.Nearly half of the M-SME are classified as having a lower level of available knowledge, and only 10% are classified with the highest level. No difference is observed between the size of the company and the level of available knowledge. This means that growth in the number of employees and sales of M-SMEs is not necessarily accompanied by an accumulation of knowledge that companies can use to improve their operations.     

Influence Diagrams for Causal Modelling and Inference

We consider a variety of ways in which probabilistic and causal models can be represented in graphical form. By adding nodes to our graphs to represent parameters, decision, etc ., we obtain a generalisation of influence diagrams that supports meaningful causal modelling and inference, and only requires concepts and methods that are already standard in the purely probabilistic case. We relate our representations to others, particularly functional models, and present arguments and examples in favour of their superiority.     

MCMC Estimation of Multi-locus Genome Sharing and Multipoint Gene Location Scores

Effective linkage detection and gene mapping requires analysis of data jointly on members of extended pedigrees, jointly at multiple genetic markers. Exact likelihood computation is then often infeasible, but Markov chain Monte Carlo (MCMC) methods permit estimation of posterior probabilities of genome sharing among relatives, conditional upon marker data. In principle, MCMC also permits estimation of linkage analysis location score curves, but in practice effective MCMC samplers are hard to find. Although the whole-meiosis Gibbs sampler (M-sampler) performs well in some cases, for extended pedigrees and tightly linked markers better samplers are needed. However, using the M-sampler as a proposal distribution in a Metropolis-Hastings algorithm does allow genetic interference to be incorporated into the analysis.     

Universal Pareto dominance and welfare for plausible utility functions

We study Pareto efficiency in a setting that involves two kinds of uncertainty: Uncertainty over the possible outcomes is modeled using lotteries whereas uncertainty over the agents’ preferences over lotteries is modeled using sets of plausible utility functions. A lottery is universally Pareto undominated if there is no other lottery that Pareto dominates it for all plausible utility functions. We show that, under fairly general conditions, a lottery is universally Pareto undominated iff it is Pareto efficient for some vector of plausible utility functions, which in turn is equivalent to affine welfare maximization for this vector. In contrast to previous work on linear utility functions, we use the significantly more general framework of skew-symmetric bilinear (SSB) utility functions as introduced by Fishburn (1982). Our main theorem generalizes a theorem by Carroll (2010) and implies the ordinal efficiency welfare theorem. We discuss three natural classes of plausible utility functions, which lead to three notions of ordinal efficiency, including stochastic dominance efficiency, and conclude with a detailed investigation of the geometric and computational properties of these notions.     

动力煤的灰分计价与发热量计价及它们之间的关系

解释煤的几个定义,分析灰分计价与发热量计价的利弊,通过发热量回归分析计算,指出发热量的预测预报必须使用同一煤样。     

中式快餐店满意度模型的实证研究

这篇文章考虑产品、用餐环境、服务等因素,建立了顾客满意度模型,提出了相应的研究假设。借助于合肥市某中式快餐店的调研数据,利用因子分析、回归分析等方法,进行了模型的实证研究。结果表明:模型中考虑的三个影响因素对顾客满意度具有显著影响。其中,产品对满意度的影响最大。     

中国消费者对于韩国移动电话态度认知要素形成的实证研究

对中国消费者对于韩国移动电话态度形成要素的相关研究,由理论、事例和实证分析三个部分组成。研究通对于态度形成的相关理论进行考察,以大量学术研究为理论背景,将已感知的品质、有用性、商标形象、危险知觉和国家形象设定为自变量,态度作为应变量,研究自变量与应变量之间的关系。事例研究以三星电子、LG电子公司为主要对象,实证部分以中国北京、上海、南京为调查基地,对普通消费者实行问卷调查。数据分析利用多元回归分析的方法。     

Adversarial and Amiable Inference in Medical Diagnosis,Reliability and Survival Analysis

In this paper, we develop a family of bivariate beta distributions that encapsulate both positive and negative correlations, and which can be of general interest for Bayesian inference. We then invoke a use of these bivariate distributions in two contexts. The first is diagnostic testing in medicine, threat detection and signal processing. The second is system survivability assessment, relevant to engineering reliability and to survival analysis in biomedicine. In diagnostic testing, one encounters two parameters that characterize the efficacy of the testing mechanism: test sensitivity and test specificity. These tend to be adversarial when their values are interpreted as utilities. In system survivability, the parameters of interest are the component reliabilities, whose values when interpreted as utilities tend to exhibit co‐operative (amiable) behavior. Besides probability modeling and Bayesian inference, this paper has a foundational import. Specifically, it advocates a conceptual change in how one may think about reliability and survival analysis. The philosophical writings of de Finetti, Kolmogorov, Popper and Savage, when brought to bear on these topics constitute the essence of this change. Its consequence is that we have at hand a defensible framework for invoking Bayesian inferential methods in diagnostics, reliability and survival analysis. Another consequence is a deeper appreciation of the judgment of independent lifetimes. Specifically, we make the important point that independent lifetimes entail at a minimum, a two‐stage hierarchical construction.