Locally efficient semiparametric estimators for a class of Poisson models with measurement error

The presence of measurement error may cause bias in parameter estimation and can lead to incorrect conclusions in data analyses. Despite a large body of literature on general measurement error problems, relatively few works exist to handle Poisson models. In this article we thoroughly study Poisson models with errors in covariates and propose consistent and locally efficient semiparametric estimators. We assess the finite sample performance of the estimators through extensive simulation studies and illustrate the proposed methodologies by analyzing data from the Stroke Recovery in Underserved Populations Study. The Canadian Journal of Statistics 47: 157–181; 2019 © 2019 Statistical Society of Canada     

A new large sample goodness of fit test for multivariate normality based on chi squared probability plots

Based on a chi square transform of the multivariate normal data set, we proposed a technique for testing multinormality which is the sum of interpoint squared distances between an ordered set of the transformed observations and the set of the population pth quantiles of the chi squared distribution. The critical values of the test were evaluated for different sample sizes and random vector dimensions through extensive simulations. The empirical type-I-error rates and powers of the proposed test were compared with those of some other well known tests for MVN with the proposed test showing excellent results at large sample sizes.     

Parametric and nonparametric methods for confidence intervals and sample size planning for win probability in parallel-group randomized trials with Likert item and Likert scale data

Data on the Likert scale are ubiquitous in medical research, including randomized trials. Statistical analysis of such data may be conducted using the means of raw scores or the rank information of the scores. In the context of parallel-group randomized trials, we quantify treatment effects by the probability that a subject in the treatment group has a better score than (or a win over) a subject in the control group. Asymptotic parametric and nonparametric confidence intervals for this win probability and associated sample size formulas are derived for studies with only follow-up scores, and those with both baseline and follow-up measurements. We assessed the performance of both the parametric and nonparametric approaches using simulation studies based on real studies with Likert item and Likert scale data. The simulation results demonstrate that even without baseline adjustment, the parametric methods did not perform well, in terms of bias, interval coverage percentage, balance of tail error, and assurance of achieving a pre-specified precision. In contrast, the nonparametric approach performed very well for both the unadjusted and adjusted win probability. We illustrate the methods with two examples: one using Likert item data and the other using Like scale data. We conclude that non-parametric methods are preferable for two-group randomization trials with Likert data. Illustrative SAS code for the nonparametric approach using existing procedures is provided.     

the Block–Block Bootstrap: Improved Asymptotic Refinements

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join‐point problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join‐point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block–block bootstrap. The asymptotic refinements of the block–block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.     

ROUTES TO HIGHER-ORDER ACCURACY IN PARAMETRIC INFERENCE

Developments in the theory of frequentist parametric inference in recent decades have been driven largely by the desire to achieve higher-order accuracy, in particular distributional approximations that improve on first-order asymptotic theory by one or two orders of magnitude. At the same time, much methodology is specifically designed to respect key principles of parametric inference, in particular conditionality principles. Two main routes to higher-order accuracy have emerged: analytic methods based on 'small-sample asymptotics', and simulation, or 'bootstrap', approaches. It is argued here that, of these, the simulation methodology provides a simple and effective approach, which nevertheless retains finer inferential components of theory. The paper seeks to track likely developments of parametric inference, in an era dominated by the emergence of methodological problems involving complex dependences and/or high-dimensional parameters that typically exceed available data sample sizes.     

Inequality, unemployment and growth: New measures for old controversies

This essay surveys some of the work of the University of Texas Inequality Project, a small research group that for the past decade has worked primarily to develop new measures of economic inequality, using a method based on the between-groups component of Theil’s T statistic. In this way, inequality statistics can be computed from many diverse and mundane sources of information, including regional tax collections, employment and earnings, census of manufacturing, and harmonized international industrial data sets. The rich data environment so constructed permits new analyses of patterns of economic change, by region, by sector, and by country, and broadly supports the idea that the movement of inequality is closely related to macroeconomic events at the national and the global level. I thank Ravi Kanbur for the invitation to submit this essay for consideration by the JOEI, and I thank the members of the UTIP team for comments on the draft.     

我国留守家庭状况的统计研究

对我国留守家庭的研究基于2000年全国人口普查0.95‰抽样数据。研究结果表明,留守家庭已经成为我国社会转型时期的重要家庭类型,其主要特征是家庭结构的变异和家庭功能的弱化。     

本科毕业论文引文分析

以提高高等院校文献信息资源建设的科学性和文献现代资源利用率为目的 ,采用文献计量学的基本理论与方法 ,对天津大学信息工程学院的微电子专业 (1993— 1997年 ) ,本科毕业论文的引文量、引文文种、引文年代及引文半衰期等 ,进行了统计分析。结果显示 ,该读者群对各类型文献利用率 :中文文献占 5 2 .1% ,外文文献占 4 6 .6 %……分析结果表明 ,高校文献保障机构应重视、调整不同类型文献资源的收藏比例 ,同时 ,本科生应强化对外文文献资源的查询与利用能力     

新时期国内美国教育研究的考察——基于2000—2009年三种中文核心期刊的分析

本文搜集了2000—2009年发表在三家中文核心期刊上美国教育研究的所有论文,从研究时段、研究对象和研究领域等方面进行了统计分析。研究结果显示：国内美国教育研究领域全面拓展,研究的深度和系统性都有极大的增强,研究者们具有较强的时代意识和现实问题研究取向。研究的热点和重点仍然集中在教育管理、高等教育等方面;越来越多的研究者将教育学科与其他学科相结合,新型交叉学科研究呈现强劲发展势头;对教育的反思呈上升态蛰：教育史肇科研究有待增强。     

基于资源和管理的技术创新能力的定义和统计分析

从能力的基本定义和实质出发, 从技术创新资源和技术创新管理的角度提出了制造业企业技术创新能力的定义, 并建立了评价指标。使用非参数统计方法证明了企业所拥有的技术创新资源及其管理水平共同决定了技术创新能力的大小, 提出了资源是基础, 管理是合理配置资源的过程, 资源和管理必须协调发展, 才能有效激活企业资源, 提高技术创新能力, 并提出了中国企业增加技术创新资源的途径, 即应注重管理创新, 从而提高技术创新能力。