The two-sided power-binomial distribution for modeling binary count data

The two-sided power (TSP) distribution is a flexible two-parameter distribution having uniform, power function and triangular as sub-distributions, and it is a reasonable alternative to beta distribution in some cases. In this work, we introduce the TSP-binomial model which is defined as a mixture of binomial distributions, with the binomial parameter p having a TSP distribution. We study its distributional properties and demonstrate its use on some data. It is shown that the newly defined model is a useful candidate for overdispersed binomial data.     

从庇护性就业到支持性就业 ——对广东省残疾人工作整合型社会企业的多个案研究

为适应经济社会的转型发展和残疾人多层次的就业与康复需求,我国残疾人就业模式日益多元化。作为一种新兴的残疾人就业模式,工作整合型社会企业旨在为残疾人提供支持性就业,以促进残疾人的社会融合。作为一种新兴的组织类型,工作整合型社会企业兼具市场、社会与政府三重维度的混合价值取向,致力于运用市场机制解决残疾人所面临的社会问题。工作整合型社会企业在创新残疾人就业模式、促进残疾人社会融合、增加残疾人经济收入、提升残疾人康复服务等方面有着积极作用,但其发展仍处于初级阶段,在专业人员培养、商业运营能力、目标人群覆盖面、政策扶持及法律保障等方面面临不少挑战。整体而言,工作整合型社会企业及其倡导的支持性就业与"重康复、轻就业"的庇护性就业模式并非全然排斥,而是一种相辅相成、前后相继的共融关系,共同塑造我国残疾人就业的多元格局。     

Psychotropic drug classification based on sleep–wake behaviour of rats

Summary.  The aim of the paper is to present methodology for the classification of potential psychotropic drugs on the basis of their activity. We first sketch the background of this class of drugs and then zoom in on so-called pharmacoelectroencephalogram studies. These data pose some statistical challenges. For classification purposes, we propose a flexible hierarchical discriminant analysis tool, allowing us to take the specific nature of the drug class into account, as well as the features of the mixed models, in combination with fractional polynomials, fitted to the electroencephalogram data. The method is evaluated against the background of existing methods. The method's performance is studied by using a comprehensive analysis of a large electroencephalogram data set.     

The identifiability of the mixed proportional hazards competing risks model

Summary. We prove identification of dependent competing risks models in which each risk has a mixed proportional hazard specification with regressors, and the risks are dependent by way of the unobserved heterogeneity, or frailty, components. We show that the conditions for identification given by Heckman and Honoré can be relaxed. We extend the results to the case in which multiple spells are observed for each subject.     

A new approach to variance component estimation with diagnostic implications

Closed form expressions are developed for the estimators of functions of the variance components in balanced, mixed, linear models. These estimators are averages of sample covariances (variances) which offer diagnostic information on the data and the model. The cause of negative estimates may be revealed. Examples illustrate the basic concepts.     

Structural Vector Autoregressions With Nonnormal Residuals

In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just-identifying in a standard SVAR framework. The results are illustrated using a U.S. macro data set and a system of U.S. and European interest rates.     

A Method for Seasonally Adjusting Time Series With Variation in the Seasonal Amplitude

The official seasonally adjusted figures of the unemployment series in the Netherlands proved to be unsatisfactory in the years 1976 until 1980 because of the occurrence of a residual seasonal pattern in the adjusted series. There is indication that this failure is due to the presence of variations in the seasonal amplitude of the unemployment series. To improve this unsatisfactory state of affairs further research on methods of seasonal adjustment was undertaken at the Netherlands Central Bureau of Statistics. The outcome, method CPBX11, combines features of two methods that have been used officially, CENSUS X-11 and CPB-1. Since December 1980 the Netherlands Central Bureau of Statistics has used CPBX11 to compute seasonally adjusted labor market series. In this article we review in short the literature on seasonal adjustment and compare the performance of the three procedures referred to above in adjusting the series Unemployment in Construction and Live Births (per 1,000 of the mean population) for the Netherlands. The CPBX11 method yields more satisfactory results, especially for the first series.     

Identification of parametric Rasch-type models

In modern Item Response Theory, the Rasch model is viewed as a Generalized Linear Mixed Model, where the item parameters correspond to the fixed-effects, whereas the person specific parameters are the random-effects. The statistical model, bearing on the observable variables only, is obtained after integrating out the random-effects. Although it is widely accepted that the parameters of this model are identified, it is hard to find a correct justification. Furthermore, the meaning of the parameters of the Rasch model – as well as of its extensions – is typically based on the fixed-effects specification of the model, that is, when the person specific parameters are also treated as fixed-effects. The contribution of this paper is to provide an explicit proof of the identification of the random-effects Rasch model. The proof is valid for a large class of Rasch-type models. It is also shown that such a proof can be applied to analyze the identification of Explanatory Rasch Models. Finally, the meaning of the parameters of interest with respect to the different data generating process is discussed.     

Residual-based specification of the random-effects distribution for cluster data

We propose a method for specifying the distribution of random effects included in a model for cluster data. The class of models we consider includes mixed models and frailty models whose random effects and explanatory variables are constant within clusters. The method is based on cluster residuals obtained by assuming that the random effects are equal between clusters. We exhibit an asymptotic relationship between the cluster residuals and variations of the random effects as the number of observations increases and the variance of the random effects decreases. The asymptotic relationship is used to specify the random-effects distribution. The method is applied to a frailty model and a model used to describe the spread of plant diseases.     

Forecasting data revisions of GDP: a mixed frequency approach

Releases of GDP data undergo a series of revisions over time. These revisions have an impact on the results of macroeconometric models documented by the growing literature on real-time data applications. Revisions of U.S. GDP data can be explained and are partly predictable according to Faust et al. (J. Money Credit Bank. 37(3):403–419, 2005) or Fixler and Grimm (J. Product. Anal. 25:213–229, 2006). This analysis proposes the inclusion of mixed frequency data for forecasting GDP revisions. Thereby, the information set available around the first data vintage can be better exploited than the pure quarterly data. In-sample and out-of-sample results suggest that forecasts of GDP revisions can be improved by using mixed frequency data.