Prior Distributions for Item Parameters in IRT Models

The focus of this article is on the choice of suitable prior distributions for item parameters within item response theory (IRT) models. In particular, the use of empirical prior distributions for item parameters is proposed. Firstly, regression trees are implemented in order to build informative empirical prior distributions. Secondly, model estimation is conducted within a fully Bayesian approach through the Gibbs sampler, which makes estimation feasible also with increasingly complex models. The main results show that item parameter recovery is improved with the introduction of empirical prior information about item parameters, also when only a small sample is available.     

环境化学课程建设的研究与实践

以新的课程理论为指导 ,构建环境化学基础理论内容、实验内容、实践内容三统一的内容体系 ,进行创新教学模式的实践 ,取得了成效     

Extended stirling family of discrete probability distributions

In this paper. introduced is a family of discrete probability distributions. whose probability function includes explieitly the Striling-Carlitz polynomial of the first or the second kind. The new family extends the stirling family of distributions. Sibuya (1988) includes the conditional distributions of the orginal ones and enlarges the application area.     

中国寿险银保业务发展模式的转变

银保业务曾经推动了寿险业务规模的迅速攀升,但随着金融环境和监管环境的变化以及受制于各种因素,近年来银保业务持续下滑,究其原因,主要还是寿险公司自身经营管理以及渠道合作模式等存在问题。在新的市场环境下,我国寿险公司要走出发展困境,一方面需要进一步明确银保业务的自身定位,另一方面要根据自身的需求确定未来的发展方向和发展模式。在此过程中,积极借鉴国外成功的银保合作模式,升级银保产品、深化客户开发、优化网点经营将成为银保模式转变的重要突破点。     

Toward a Hybrid Model for Effective Organizing in Communities of Color

This paper examines the feminist and Alinsky approaches to community organizing, both of which have had great impact on the field of organizing. Viewing the two models as flexible and interflowing, the authors then propose a hybrid organizing model that draws from the strengths of both approaches. The model also calls for continual assessment of the changing organizing context and flexibility to change the blend of strategies according to the needs of the moment. Finally, the paper looks at how the dynamics of race and ethnicity affect organizing in communites of color, and discusses the usefulness of the proposed model in dealing with these dynamics.     

OPTIMAL IV ESTIMATION OF SYSTEMS WITH STOCHASTIC REGRESSORS AND VAR DISTURBANCES WITH APPLICATIONS TO DYNAMIC SYSTEMS

This paper considers the general problem of Feasible Generalized Least Squares Instrumental Variables (FGLS IV) estimation using optimal instruments. First we summarize the sufficient conditions for the FGLS IV estimator to be asymptotically equivalent to an optimal GLS IV estimator. Then we specialize to stationary dynamic systems with stationary VAR errors, and use the sufficient conditions to derive new moment conditions for these models. These moment conditions produce useful IVs from the lagged endogenous variables, despite the correlation between errors and endogenous variables. This use of the information contained in the lagged endogenous variables expands the class of IV estimators under consideration and thereby potentially improves both asymptotic and small-sample efficiency of the optimal IV estimator in the class. Some Monte Carlo experiments compare the new methods with those of Hatanaka (1976). For the DGP used in the Monte Carlo experiments, asymptotic efficiency is strictly improved by the new IVs, and experimental small-sample efficiency is improved as well.     

Autoregressiv model identification for multivariate time series

A procedure is developed for the identification of autoregressive models for stationary invertible multivariate Gaussian time series. Model selection is based on either the AIC information criterion or on a statistic called CVR, cross-validatory residual sum of squares. An example is given to show that the forecasts generated by these models compare favorably with those generated by other common time series modeling techniques.     

On probit versus logit dynamic mixed models for binary panel data

In this paper, we consider inferences in a binary dynamic mixed model. The existing estimation approaches mainly estimate the regression effects and the dynamic dependence parameters either through the estimation of the random effects or by avoiding the random effects technically. Under the assumption that the random effects follow a Gaussian distribution, we propose a generalized quasilikelihood (GQL) approach for the estimation of the parameters of the dynamic mixed models. The proposed approach is computationally less cumbersome than the exact maximum likelihood (ML) approach. We also carry out the GQL estimation under two competitive, namely, probit and logit mixed models, and discuss both the asymptotic and small-sample behaviour of their estimators.     

Maximum Likelihood Estimation for Multinomial‐Poisson Models: A Generalization of Birch's Numerical Invariance Results

Abstract. This study gives a generalization of Birch's log‐linear model numerical invariance result. The generalization is given in the form of a sufficient condition for numerical invariance that is simple to verify in practice and is applicable for a much broader class of models than log‐linear models. Unlike Birch's log‐linear result, the generalization herein does not rely on any relationship between sufficient statistics and maximum likelihood estimates. Indeed the generalization does not rely on the existence of a reduced set of sufficient statistics. Instead, the concept of homogeneity takes centre stage. Several examples illustrate the utility of non‐log‐linear models, the invariance (and non‐invariance) of fitted values, and the invariance (and non‐invariance) of certain approximating distributions.