Profile of the Present Slovak Society and its Regional Differences

The article is the first attempt in assessment of the development and the present situation of social structure and stratification in Slovakia. Its principal aim is to develop the profile of the present society in Slovakia, which reflects the pace and complexity of the transformation process and its impact on society. Processing of Micro census data from 2003 led to creation of the profile of society in Slovakia at the national level based on income distribution but also knowledge on regional societies and differences between them were obtained. It was found out that the present profile of the society in Slovakia is pear-shaped which means strong representation of lower strata and weaker representation of higher middle and high strata. The profile disclosed some specific features and inconsistencies in the present society of Slovakia, which were, and still are determined by many non standard past and present phenomena and processes often disguised by reforms. The information drawn in regions has facilitated identification of the basic nature and structure of regional societies, some of their specific features and negative elements and to demonstrate their highly differentiated character.

A propensity score matching analysis of the relationship between victim sex and capital juror decision-making in North Carolina

A small body of prior research has examined the impact of victim sex on jury death penalty decision-making and the majority of this research has demonstrated some evidence of a “female victim effect” such that cases involving a female victim are more likely to receive the death penalty than similarly situated cases with a male victim. However, within this line of research studies have suggested that victim sex may work in conjunction with other case characteristics. In order to further explore this phenomenon, the current study examines a near-population of death penalty cases from North Carolina (n = 1069) from 1977–2009 using propensity score matching. Results demonstrate that once cases are matched on more than 50 legal and extralegal case characteristics, there is no statistically significant or substantive link between victim sex and death penalty decision-making. Findings suggest that it is concrete differences in the legal and extralegal factors observed in cases with female victims compared to male victims that shape jury death sentence decisions rather than a direct effect of victim sex (before matching: OR = 1.53; 95% CI = 1.20–1.95; p < .001/after matching: OR = 0.90; 95% CI = 0.66–1.24; p = .52). Study limitations and implications are also discussed.     

企业业绩评价方法改进的探讨

企业绩效评价在促进企业改善经营管理、提高经济效益方面具有不可替代的作用。以利润为中心的传统绩效评价指标不能真实地反映公司经营业绩,经济增加值(EVA)和平衡计分卡(BSC)虽然较传统的绩效评价方法有了很大改进,但仍然存在一些不足。将EVA和BSC结合,形成EVA计分卡,运用层次分析法和专家评分法量化指标权重,能较好地评价企业业绩。     

留守经历对新生代农民工工作流动的影响--基于2015年北京市数据的实证检验

本文基于2015年北京市新生代农民工的微观调查数据，在利用倾向得分倒数加权法纠正样本的选择性偏差以后发现：（1）新生代农民工过高的工作流动频率在一定程度上与其普遍的留守经历有关；（2）不同类型的留守形式和留守时间对工作流动的影响有所不同，完全留守、长期留守者表现出更高的工作流动性；（3）相同的留守行为对工作流动的影响存在明显的性别差异，男性农民工更易受到童年时期留守经历的影响。上述结论的政策启示在于：在关注留守经历及其所带来的影响差异的同时，也应避免留守问题的代际传承。     

Estimating the effectiveness of a misdemeanor drug diversion program using propensity score matching and survival analyses

This study analyzes three years of data on misdemeanor drug offenders in Winnebago County, Wisconsin. A portion of these offenders opted into a Misdemeanor Drug Diversion Program (MDDP) offered instead of traditional adjudication. Recidivism in the treatment and comparison groups is estimated using standard binary response techniques augmented with propensity score matching to address selection bias. Results show that the MDDP reduces the probability of re-offense by 16%, after adjusting for possible selection bias. Cox proportional hazard modeling is also used to assess time-to-re-offense differentials between the treatment and comparison groups. The survival analysis indicates that the hazard rate of re-offense is 60% lower per day among those treated with the MDDP program than those who did not complete the program. The average number of days to re-offense among those that do re-offend is 297 days in the treatment group and 203 days in the comparison group.     

Prediction intervals in exponential families

A method for obtaining prediction intervals for an outcome of a future experiment is presented. The method uses hypothesis testing as a tool to derive prediction intervals and assumes that the probability distributions of informative and future experiments are one parameter exponential families. Asymptotic similar mean coverage prediction intervals are derived using the score test as a test statistics. Examples are presented and asymptotic prediction limits are compared with the prediction limits given in the literature.     

Ranking paired contestants

The ranking of paired contestants (players) after a series of contests is difficult when every player does not play every other player. In the 1975 JASA Mark Thompson presented a maximum likelihood solution based on the assumption that the probability of any one player defeating any other is a function only of the difference in their ranks. Here the linear approximation to that likelihood is shown to lead to a nonparametric measure of the efficacy of the ranking, called the net difference in ranks (NDR) , which is the sum of the differences in ranks of the paired players in the observed contests that agree with the ranking minus the sum of the differences in ranks in the observed contests that disagree with the ranking (upsets) . The subject is part of a large literature that has been consolidated by H.A. David in The Method of Paired Comparisons (1963, 1988). The method was introduced by the psychophysicist Fechner in 1860 and has been widely applied to sensory testing,     

Bayesian Analysis in Regression Models Using Pseudo-Likelihoods

This article deals with the issue of using a suitable pseudo-likelihood, instead of an integrated likelihood, when performing Bayesian inference about a scalar parameter of interest in the presence of nuisance parameters. The proposed approach has the advantages of avoiding the elicitation on the nuisance parameters and the computation of multidimensional integrals. Moreover, it is particularly useful when it is difficult, or even impractical, to write the full likelihood function.

We focus on Bayesian inference about a scalar regression coefficient in various regression models. First, in the context of non-normal regression-scale models, we give a theroetical result showing that there is no loss of information about the parameter of interest when using a posterior distribution derived from a pseudo-likelihood instead of the correct posterior distribution. Second, we present non trivial applications with high-dimensional, or even infinite-dimensional, nuisance parameters in the context of nonlinear normal heteroscedastic regression models, and of models for binary outcomes and count data, accounting also for possibile overdispersion. In all these situtations, we show that non Bayesian methods for eliminating nuisance parameters can be usefully incorporated into a one-parameter Bayesian analysis.     

Diagnostics for elliptical linear mixed models with first-order autoregressive errors

For longitudinal time series data, linear mixed models that contain both random effects across individuals and first-order autoregressive errors within individuals may be appropriate. Some statistical diagnostics based on the models under a proposed elliptical error structure are developed in this work. It is well known that the class of elliptical distributions offers a more flexible framework for modelling since it contains both light- and heavy-tailed distributions. Iterative procedures for the maximum-likelihood estimates of the model parameters are presented. Score tests for the presence of autocorrelation and the homogeneity of autocorrelation coefficients among individuals are constructed. The properties of test statistics are investigated through Monte Carlo simulations. The local influence method for the models is also given. The analysed results of a real data set illustrate the values of the models and diagnostic statistics.     

Comparison of causal effect estimators under exposure misclassification

Over the past decades, various principles for causal effect estimation have been proposed, all differing in terms of how they adjust for measured confounders: either via traditional regression adjustment, by adjusting for the expected exposure given those confounders (e.g., the propensity score), or by inversely weighting each subject's data by the likelihood of the observed exposure, given those confounders. When the exposure is measured with error, this raises the question whether these different estimation strategies might be differently affected and whether one of them is to be preferred for that reason. In this article, we investigate this by comparing inverse probability of treatment weighted (IPTW) estimators and doubly robust estimators for the exposure effect in linear marginal structural mean models (MSM) with G-estimators, propensity score (PS) adjusted estimators and ordinary least squares (OLS) estimators for the exposure effect in linear regression models. We find analytically that these estimators are equally affected when exposure misclassification is independent of the confounders, but not otherwise. Simulation studies reveal similar results for time-varying exposures and when the model of interest includes a logistic link.