Robust Priors in Nonlinear Panel Data Models

Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall into this category. We provide a characterization of the class of weights (or priors) that produce estimators that are first‐order unbiased. We show that such bias‐reducing weights will depend on the data in general unless an orthogonal reparameterization or an essentially equivalent condition is available. Two intuitively appealing weighting schemes are discussed. We argue that asymptotically valid confidence intervals can be read from the posterior distribution of the common parameters when N and T grow at the same rate. Next, we show that random effects estimators are not bias reducing in general and we discuss important exceptions. Moreover, the bias depends on the Kullback–Leibler distance between the population distribution of the effects and its best approximation in the random effects family. Finally, we show that, in general, standard random effects estimation of marginal effects is inconsistent for large T, whereas the posterior mean of the marginal effect is large‐T consistent, and we provide conditions for bias reduction. Some examples and Monte Carlo experiments illustrate the results.     

Estimation of coefficient of dispersion using auxiliary information

In this note, we consider the problem of estimation of coefficient of dispersion of a study variable by making use of known value of the coefficient of dispersion of an auxiliary variable. We propose ratio and regression-type estimators. We derive expressions of bias and variance of the proposed estimators to the first order of approximation. The relative efficiencies of the ratio and regression-type estimators with respect to the naïve estimator are investigated through a simulation study.     

Bias and size corrections in extreme value modeling

Extreme value theory models have found applications in myriad fields. Maximum likelihood (ML) is attractive for fitting the models because it is statistically efficient and flexible. However, in small samples, ML is biased to O(N^?1) and some classical hypothesis tests suffer from size distortions. This paper derives the analytical Cox–Snell bias correction for the generalized extreme value (GEV) model, and for the model's extension to multiple order statistics (GEVr). Using simulations, the paper compares this correction to bootstrap-based bias corrections, for the generalized Pareto, GEV, and GEVr. It then compares eight approaches to inference with respect to primary parameters and extreme quantiles, some including corrections. The Cox–Snell correction is not markedly superior to bootstrap-based correction. The likelihood ratio test appears most accurately sized. The methods are applied to the distribution of geomagnetic storms.     

Challenges of social workers' involvement in the recovery of the 5.12 Wenchuan Earthquake in China

This article explores the challenges of social workers' involvement in recovery work of the 5.12 Wenchuan Earthquake (12 May 2008). Six social workers working in three social work stations in Sichuan, China, were asked to report the challenges they faced in working with disaster survivors. Findings reveal that the social workers faced many challenges. These include lack of government support, low professional status of social work, rapid changes in the social environment in disaster‐affected areas, lack of supervision, lack of cooperation and coordination among social service agencies, and lack of experience and knowledge in working with disaster survivors. The practical, educational and policy implications of the findings are addressed.     

Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction

Verification bias may occur when the test results of not all subjects are verified by using a gold standard. The correction for this bias can be made using different approaches depending on whether missing gold standard test results are random or not. Some of these approaches with binary test and gold standard results include the correction method by Begg and Greenes, lower and upper limits for diagnostic measurements by Zhou, logistic regression method, multiple imputation method, and neural networks. In this study, all these approaches are compared by employing a real and simulated data under different conditions.     

Covariate Decomposition Methods for Longitudinal Missing‐at‐Random Data and Predictors Associated with Subject‐Specific Effects

Investigators often gather longitudinal data to assess changes in responses over time within subjects and to relate these changes to within‐subject changes in predictors. Missing data are common in such studies and predictors can be correlated with subject‐specific effects. Maximum likelihood methods for generalized linear mixed models provide consistent estimates when the data are ‘missing at random’ (MAR) but can produce inconsistent estimates in settings where the random effects are correlated with one of the predictors. On the other hand, conditional maximum likelihood methods (and closely related maximum likelihood methods that partition covariates into between‐ and within‐cluster components) provide consistent estimation when random effects are correlated with predictors but can produce inconsistent covariate effect estimates when data are MAR. Using theory, simulation studies, and fits to example data this paper shows that decomposition methods using complete covariate information produce consistent estimates. In some practical cases these methods, that ostensibly require complete covariate information, actually only involve the observed covariates. These results offer an easy‐to‐use approach to simultaneously protect against bias from both cluster‐level confounding and MAR missingness in assessments of change.     

限制兼任教师与民国大学学术职业发展

民国初期,政府通过教学工作量将大学教师职业划分为专任教师和兼任教师两种类型。当时,大学盛行聘请兼任教师及专任教师在外校兼课,兼任教师和专任教师之间的区别不明显。这种现象受到了海内外学者的批评,此后,中国的学界、大学和政府逐渐限制兼任教师:学界注重构筑官职和学术职业的界限,限制大学专任教师兼任官职|大学约束本校教师外出兼课|政府限制大学聘请过多的兼任教师。到民国后期,中国大学学术职业的专任程度逐步提高。这一历程反映了大学由注重教学绩效向关注学术质量的转变。     

Resampling-based bias-corrected time series prediction

In this paper, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of (possibly) nonlinear functions of finitely many future observations in a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy. Finite sample properties of the method are investigated through a simulation study.     

美国大学校长职业变迁：一种历史的视角

大学校长在引领美国大学的发展过程中发挥着至关重要的作用。美国高等教育的发展史,也可以说是美国大学校长职业的变迁史。纵观美国高等教育发展历程,大学校长职业发展先后经历了牧师兼职型校长、学者领导型校长、专家管理型校长、职业经理型校长四个时期,并且随着社会结构和大学组织自身的变化,大学校长角色越来越向职业经理型校长转变。     

基于平衡单水平轮换的连续性抽样估计方法研究

内容提要：针对现存的各种单水平轮换模式和估计方法,本文提出一套统一的平衡单水平轮换模式。在此轮换模式下,引入两类相关关系,运用线性无偏估计方法,并通过使不同类型估计量方差的加权总和最小的方法确定最优系数,从而得到最优线性无偏估计量,不仅能够减少甚至消除估计量偏差的影响,还能使得连续性调查的整体抽样误差最小,适合估计各种类型的估计量。