Maximum entropy in the mean methods in propensity score matching for interval and noisy data

Abstract

In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to handle explicitly cases when data is available: (i) in interval form; (ii) with bounded measurement or observational errors; or (iii) both as intervals and with bounded errors. We show that entropy in the mean methods for these three cases generally outperform benchmark error-free approaches.     

BARTLETT-TYPE CORRECTIONS FOR TWO-PARAMETER EXPONENTIAL FAMILY MODELS

ABSTRACT

Cordeiro and Ferrari[1] Cordeiro, G.M. and Ferrari, S.L.P. 1991. A Modified Score Test Statistic Having Chi-Squared Distribution to Order n^?1. Biometrika, 78: 573–582. [Web of Science ®] , [Google Scholar] obtained a Bartlett-type correction to the score statistic that is given by a polynomial of second degree in the statistic itself with coefficients that depend on cumulants of log–likelihood derivatives. Although the corrected statistic has good size properties, it is not always a monotone transformation of the original statistic. Recently, some monotone transformations of the score statistic have been proposed as alternatives to the polynomial transformation. In this paper we derive simple formulae for various modified score statistics for testing a scalar parameter in two-parameter exponential models which do not require knowledge of cumulants. The formulae are readily applicable to cover many important and commonly used distributions and involve only trivial operations on certain functions and their derivatives.     

Projecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics

This article proposes and evaluates two new methods of reweighting preliminary data to obtain estimates more closely approximating those derived from the final data set. In our motivating example, the preliminary data are an early sample of tax returns, and the final data set is the sample after all tax returns have been processed. The new methods estimate a predicted propensity for late filing for each return in the advance sample and then poststratify based on these propensity scores. Using advance and complete sample data for 1982, we demonstrate that the new methods produce advance estimates generally much closer to the final estimates than those derived from the current advance estimation techniques. The results demonstrate the value of propensity modeling, a general-purpose methodology that can be applied to a wide range of problems, including adjustment for unit nonresponse and frame undercoverage as well as statistical matching.     

Robust Transformations in Univariate and Multivariate Time Series

It is well known that transformation of the response may improve the homogeneity and the approximate normality of the errors. Unfortunately, the estimated transformation and related test statistic may be sensitive to the presence of one, or several, atypical observations. In addition, it is important to remark that outliers in one transformed scale may not be atypical in another scale. Therefore, it is important to choose a transformation which does not depend on the presence of particular observations. In this article we suggest an efficient procedure based on a robust score test statistic which quantifies the effect of each observation on the choice of the transformation.     

Shrinkage of Variance for Minimum Distance Based Tests

This paper promotes information theoretic inference in the context of minimum distance estimation. Various score test statistics differ only through the embedded estimator of the variance of estimating functions. We resort to implied probabilities provided by the constrained maximization of generalized entropy to get a more accurate variance estimator under the null. We document, both by theoretical higher order expansions and by Monte-Carlo evidence, that our improved score tests have better finite-sample size properties. The competitiveness of our non-simulation based method with respect to bootstrap is confirmed in the example of inference on covariance structures previously studied by Horowitz (1998 Horowitz , J. ( 1998 ). Bootstrap methods for covariance structures . The Journal of Human Resources 33 : 39 – 61 .[Crossref], [Web of Science ®] , [Google Scholar]).     

Statistical Analysis for the Inverse Trinomial Distribution

This article considers parameter estimation, goodness of fit, likelihood ratio and score tests, and model selection by Akaike information criterion for the inverse trinomial (IT) distribution, a classical one-dimensional random walk distribution. The IT distribution has a cubic variance function of the mean and is a generalization of the negative binomial distribution. Basic distributional properties and expressions for the probability mass function, recurrence formula, moments, and score functions are also presented.     

我国城镇居民文化消费需求实证分析

利用扩展线性支出系统模型(ELES),借助SPSS13.0统计软件对2004—2010年我国城镇居民文化消费的边际消费倾向、需求收入弹性、基本需求进行实证分析,得出我国城镇居民人均年可支配收入和人均年文化消费支出高度相关等结论。建议通过提高城镇居民可支配收入、加强城镇文化基础设施建设、加强文化产品供给、发展教育、培养城镇居民正确的文化消费观等一系列措施,进一步促进城镇居民文化消费需求。     

社区公共服务对老年人幸福感的影响

本文基于中国老年健康影响因素跟踪调查(CLHLS)2018年数据,运用Ordered probit模型和工具变量条件混合过程估计法(CMP方法)考察社区公共服务对老年人幸福感的影响。研究表明,社区公共服务完善程度对老年人幸福感具有显著的促进作用,且这一影响具有明显的城乡、独居养老成因和年龄异质性。社区公共服务匹配度对社区公共服务的老年幸福感效应具有明显调节作用:社区公共服务匹配度越高,社区公共服务对老年人幸福感的促进作用越强。因此,构建以社区为依托的多层次养老体系不仅要着力完善社区公共服务,更要重视社区公共服务供给与老年居民真实需求间的精准匹配,以在提升资源利用效率的同时更好地发挥社区公共服务对老年居民福祉的促进作用。