A latent variable model for analyzing mixed longitudinal (k,l)-inflated count and ordinal responses

A random effects model for analyzing mixed longitudinal count and ordinal data is presented where the count response is inflated in two points (k and l) and an (k,l)-Inflated Power series distribution is used as its distribution. A full likelihood-based approach is used to obtain maximum likelihood estimates of parameters of the model. For data with non-ignorable missing values models with probit model for missing mechanism are used.The dependence between longitudinal sequences of responses and inflation parameters are investigated using a random effects approach. Also, to investigate the correlation between mixed ordinal and count responses of each individuals at each time, a shared random effect is used. In order to assess the performance of the model, a simulation study is performed for a case that the count response has (k,l)-Inflated Binomial distribution. Performance comparisons of count-ordinal random effect model, Zero-Inflated ordinal random effects model and (k,l)-Inflated ordinal random effects model are also given. The model is applied to a real social data set from the first two waves of the national longitudinal study of adolescent to adult health (Add Health study). In this data set, the joint responses are the number of days in a month that each individual smoked as the count response and the general health condition of each individual as the ordinal response. For the count response there is incidence of excess values of 0 and 30.     

A fistful of euros: Is the German one‐euro job workfare scheme effective for participants?

Hohmeyer K, Wolff J. A fistful of euros: is the German one‐euro job workfare scheme effective for participants? Welfare reforms have constituted a major policy issue in many OECD countries in recent decades. In Germany, a major reform in 2005 emphasised the activation of welfare recipients and introduced a workfare programme –‘One‐Euro Jobs’– on a large scale. In the present study, the impact of one‐euro jobs on the employment prospects of different groups of participants was estimated. The analysis was conducted on a large sample of welfare recipients using propensity score matching. The sample of one‐euro job participants and other welfare recipients was drawn from administrative records comprising all those who started their participation in the programme in early 2005. Our results showed that participation slightly improved the medium‐term employment prospects for women but not for men. Participation reduced the employment rate of participants younger than 25 years but raised it for some of the older participant groups. In conclusion, one‐euro jobs are effective for participants who have been jobless for several years but ineffective for participants who were recently employed.     

Simulation techniques for generalized Gaussian densities

This contribution deals with the Monte Carlo simulation of generalized Gaussian random variables. Such a parametric family of distributions has been proposed in many applications in science to describe physical phenomena and in engineering, and it seems to be also useful in modelling economic and financial data. For values of the shape parameter α within a certain range, the distribution presents heavy tails. In particular, the cases α=1/3 and α=1/2 are considered. For such values of the shape parameter, different simulation methods are assessed.     

《雷雨》人物对话研究

戏剧《雷雨》中有大量的人物对话,在刻画人物性格,表现人物内心世界,推进戏剧冲突方面起到了关键作用。文章主要以周萍为主线,考察他同繁漪、鲁侍萍的对话,对戏中人物对话进行审美分析。     

宗教、邪教与文化的挤占效应分析

从历史经验看,中国古代传统主流文化存在"信仰缺乏症",带有宗教特征的巫文化与主流文化结合,部分地满足了人们的宗教信仰需求,表现出对传统主流文化的占位效应。在当下中国,由于大量具有宗教情结人口的存在,宗教信仰作为一项次好的选择,能够发挥对邪教的文化挤出效应。     

An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.     

Complete moment convergence and Lr convergence for asymptotically almost negatively associated random variables

In this paper, we investigate the complete moment convergence and L_r convergence for maximal partial sums of asymptotically almost negatively associated random variables under some general conditions. The results obtained in the paper generalize some corresponding ones for negatively associated random variables.     

Monte carlo estimates of the distributions of the random polygons of the voronoi tessellation with respect to a poisson process

A large-scale study, in which two million random Voronoi polygons (with respect to a homogeneous Poisson point process) were generated and mensurated, is described. The polygon characteristics recorded are number of sides (or vertices), perimeter, area and interior angles. A feature is the efficient “quantile” method of replicating Poisson-type random structures, which it is hoped may find useful application elsewhere.     

Attributing Outcomes to Social Policy Interventions – ‘Gold Standard’ or ‘Fool's Gold’ in Public Policy and Management?

In recent years there has been increased interest in outcome‐based social policy‐making and management. The UK has been in the forefront of this movement but similar movements have been identified internationally. This interest in outcome‐based decision‐making has been given particular impetus through the ‘results’‐based movement in evaluation and performance management since the 1980s, which has increased in scope over time, slowly changing its emphasis from cost reduction and measuring outputs to measuring outcomes. This change has been widely welcomed by policymakers, practitioners and academics. However, there is evidence that the reality is often rather less than the rhetoric. Moreover, the ‘attribution problem’ of attributing changes in outcomes to specific social policies has remained a major issue. The conceptual solution of constructing ‘cause‐and‐effect’ models, imported from the policy evaluation field, has only recently become common for operationalising these models. This article outlines the evolution of interest in outcome‐based social policy‐making up to recent times and the growing realization of the importance of the attribution problem. It then outlines both how the ‘cause‐and‐effect’ policy modelling approach can partially tackle the attribution problem, but also its inherent limitations. Lastly, the article uses several case studies in current UK social policy‐making to demonstrate the potential importance of the reasoning embedded within cause‐and‐effect models but also the dangers in policy‐making which adopts this approach without understanding its conceptual basis or in fields where it is inappropriate, given the current state of our knowledge of social policy systems.