Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point

Recent work on point processes includes studying posterior convergence rates of estimating a continuous intensity function. In this article, convergence rates for estimating the intensity function and change‐point are derived for the more general case of a piecewise continuous intensity function. We study the problem of estimating the intensity function of an inhomogeneous Poisson process with a change‐point using non‐parametric Bayesian methods. An Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain estimates of the intensity function and the change‐point which is illustrated using simulation studies and applications. The Canadian Journal of Statistics 47: 604–618; 2019 © 2019 Statistical Society of Canada     

Deaf access to justice in Northern Ireland: rethinking ‘Reasonable Adjustment’ in the Disability Discrimination Act

Abstract

This article provides findings of a qualitative study exploring the interactions of eight Deaf participants and one hearing ally with the justice system in Northern Ireland, where the Disability Discrimination Act requires solicitors to make ‘reasonable adjustments’ in order to provide effective access to Deaf clients. Three thematic categories emerged: (a) barriers to accessing justice, (b) work Deaf people do for access, and (c) the need to educate solicitors about access. A central strain ran through these themes: the idea that ‘reasonable adjustment’ must reflect the value of sign language interpreters in facilitating effective communication access for all the parties.     

Asymptotic properties of the maximum-likelihood estimator in zero-inflated binomial regression

The zero-inflated binomial (ZIB) regression model was proposed to account for excess zeros in binomial regression. Since then, the model has been applied in various fields, such as ecology and epidemiology. In these applications, maximum-likelihood estimation (MLE) is used to derive parameter estimates. However, theoretical properties of the MLE in ZIB regression have not yet been rigorously established. The current paper fills this gap and thus provides a rigorous basis for applying the model. Consistency and asymptotic normality of the MLE in ZIB regression are proved. A consistent estimator of the asymptotic variance–covariance matrix of the MLE is also provided. Finite-sample behavior of the estimator is assessed via simulations. Finally, an analysis of a data set in the field of health economics illustrates the paper.     

海南岛中部地区生态建设的现状、问题与对策

本文结合海南岛中部地区的生态现状,分析其生态建设所面临的主要问题,提出发展生态特色农业与森林生态旅游业、实施生态移民政策、加强自然保护区建设等对策建议。     

中国基本医疗保险的实际参保率及其分布特征:基于多源数据的分析

全民覆盖是基本医疗保险降低国民医疗费用负担的前提条件。中国基本医疗保险是否实现了全民覆盖仍存在争议。本文利用多源调查数据,分析了我国基本医疗保险实际参保率及其分布特征。不同来源的数据基本证实,2015-2016年,中国仍有超过10%的国民没有参加任何一项基本医疗保险制度。其中,城镇居民、非农户口和没有户口的居民、东北地区、年轻人、儿童、未就业人群、低收入人群、在校学生以及流动人口基本医疗保险实际参保率更低。城乡居民实行自愿参保,因管理部门分割、信息系统不统一导致的重复参保以及因财政补贴制度导致的户籍地参保等制度设计,是导致基本医疗保险未能实现全民覆盖的根源。要实现基本医疗保险全民覆盖,解决国民医疗后顾之忧,未来我国基本医疗保险应实行强制参保、以家庭为单元参保、常住地参保,建立全国统一的基本医疗保险信息系统,并鼓励发展补充性医疗保障制度。     

Multiplicative consistency analysis for interval fuzzy preference relations: A comparative study

To examine the multiplicative consistency of interval fuzzy preference relations (IFPRs), this paper first analyzes the limitations associated with the previous consistency concepts. Accordingly, a new consistency concept is defined that is an extension of the crisp case and overcomes limitations in the previous concepts. Next, a linear programming model to judge the consistency of IFPRs is constructed, and an approach to derive multiplicative consistent IFPRs is introduced. Furthermore, goal-programming models to determine missing values in an incomplete IFPR are constructed that have the highest consistent level with respect to known values. Moreover, a multiplicative consistency and consensus based method for group decision making with IFPRs is developed that can address incomplete and inconsistent cases. Finally, two practical decision-making problems are offered to demonstrate the feasibility and efficiency of the new method, and an analysis of a numerical and theoretical comparison with several related methods is performed.     

Optimal Contracts when Enforcement is a Decision Variable

This paper analyzes choice‐theoretic costly enforcement in an intertemporal contracting model with a differentially informed investor and entrepreneur. An intertemporal contract is modeled as a mechanism in which there is limited commitment to payment and enforcement decisions. The goal of the analysis is to characterize the effect of choice‐theoretic costly enforcement on the structure of optimal contracts. The paper shows that simple debt is the optimal contract when commitment is limited and costly enforcement is a decision variable (Theorem 1). In contrast, stochastic contracts are optimal when agents can commit to the ex‐ante optimal decisions (Theorem 2). The paper also shows that the costly state verification model can be viewed as a reduced form of an enforcement model in which agents choose payments and strategies as part of a perfect Bayesian Nash equilibrium.     

基于模糊判断矩阵信息确定专家权重的方法

本文对群组模糊判断矩阵集结过程中确定专家权重的问题进行了研究,建立了模糊判断矩阵的特征矩阵和求解群集结矩阵的最优化模型,通过矩阵之间距离度量判断信息自身逻辑一致性程度和群体相容性程度,给出一种基于专家判断信息的可信度计算其后验权重的方法,最后用算例予以说明.