“民工荒”现象成因及对策分析

从去年下半年开始出现的“民工荒”打破了我国劳动力“无限供给”的神话,对我国未来经济增长产生了不容忽视的影响。本文对“民工荒”之所以出现作了原因分析,认为当前宏观经济形势和新的二元社会结构是导致“民工荒”的深层次原因,并对此提出了相应的对策。     

A class of saturated row–column designs

A new class of row–column designs is proposed. These designs are saturated in terms of eliminating two-way heterogeneity with an additive model. The (m,s)-criterion is used to select optimal designs. It turns out that all (m,s)-optimal designs are binary. Square (m,s)-optimal designs are constructed and they are treatment-connected. Thus, all treatment contrasts are estimable regardless of the row and column effects.     

On the Breslow–Holubkov estimator

Breslow and Holubkov (J Roy Stat Soc B 59:447–461 1997a) developed semiparametric maximum likelihood estimation for two-phase studies with a case–control first phase under a logistic regression model and noted that, apart for the overall intercept term, it was the same as the semiparametric estimator for two-phase studies with a prospective first phase developed in Scott and Wild (Biometrica 84:57–71 1997). In this paper we extend the Breslow–Holubkov result to general binary regression models and show that it has a very simple relationship with its prospective first-phase counterpart. We also explore why the design of the first phase only affects the intercept of a logistic model, simplify the calculation of standard errors, establish the semiparametric efficiency of the Breslow–Holubkov estimator and derive its asymptotic distribution in the general case.     

论意象与联想力、想象力之互动——以“多”、“二”、“一（0）”螺旋结构切入作考察

联想力与想象力是思维力的两大翅膀,藉以让思维力遨游于客观与主观时空,以通贯心、物,上撤其本、下撤其末,将真实世界、伦理世界与艺术世界融通为一,以呈现真、善、美的圆融境域.而意与象即相当于心与物,为人类思维活动的原动力,自然和联想力与想象力关系密切.大体说来,就在联想力与想象力的作用下,意象得以形成、表现、组织与统合;其中意象之形成、表现,关涉到偏于主观的"联想力与想象力"所形成之形象思维;意象之组织,关涉到偏于客观的"联想力与想象力"所形成之逻辑思维;而意象之统合,则关涉到合客观与主观为一的"联想力与想象力"所形成之综合思维.由此可知意象与联想力、想象力,在思维力的大力牵合下,不但三位一体,而且使它们形成"意象←→联想力、想象力"的"多"、"二"、"一(0)"螺旋结构.     

Logistic regression and other discrete data models for serially correlated observations

Summary We consider the analysis of discrete serially correlated data in the presence of time dependent covariates. If the interest is to relate the covariates to the marginal distribution of the data, Markov chains are an obvious tool to consider, but their use is complicated by the fact that they are expressed in terms of transitional rather than marginal probabilities. We show how to parametrize the transition matrix in a suitable way so that interpretation is as desired. The focus is on binary and Poisson data, but the methodology can be adopted also with other discrete data distributions.     

二元(二阶)线性递归关系解的周期性的简便判别法

本文给出了判别二元(二阶)线性递归关系解的周期的简便方法。     

Peer and Selection Effects on Youth Smoking in California

Previous research has found that youth smoking choices are strongly influenced by peer smoking. However, these studies often fail to account for simultaneity and nonrandom peer selection. This article describes an equilibrium model of peer effects that incorporates both of these features, and estimates its parameters using data on California teenagers. Identification is aided by using the influence of observable variables on group selection as a proxy for the influence of unobservables. I find that the effect of peer smoking on the decision to smoke is much weaker than found in previous studies.     

New tests for stochastic order with application to case control studies

It is shown that if a binary regression function is increasing then retrospective sampling induces a stochastic ordering of the covariate distributions among the responders, which we call cases, and the non-responders, which we call controls. We also show that if the covariate distributions are stochastically ordered then the regression function must be increasing. This means that testing whether the regression function is monotone is equivalent to testing whether the covariate distributions are stochastically ordered. Capitalizing on these new probabilistic observations we proceed to develop two new non-parametric tests for stochastic order. The new tests are based on either the maximally selected, or integrated, chi-bar statistic of order one. The tests are easy to compute and interpret and their large sampling distributions are easily found. Numerical comparisons show that they compare favorably with existing methods in both small and large samples. We emphasize that the new tests are applicable to any testing problem involving two stochastically ordered distributions.     

Multivariate tests comparing binomial probabilities, with application to safety studies for drugs

Summary.  In magazine advertisements for new drugs, it is common to see summary tables that compare the relative frequency of several side-effects for the drug and for a placebo, based on results from placebo-controlled clinical trials. The paper summarizes ways to conduct a global test of equality of the population proportions for the drug and the vector of population proportions for the placebo. For multivariate normal responses, the Hotelling T ²-test is a well-known method for testing equality of a vector of means for two independent samples. The tests in the paper are analogues of this test for vectors of binary responses. The likelihood ratio tests can be computationally intensive or have poor asymptotic performance. Simple quadratic forms comparing the two vectors provide alternative tests. Much better performance results from using a score-type version with a null-estimated covariance matrix than from the sample covariance matrix that applies with an ordinary Wald test. For either type of statistic, asymptotic inference is often inadequate, so we also present alternative, exact permutation tests. Follow-up inferences are also discussed, and our methods are applied to safety data from a phase II clinical trial.     

BINARY REGRESSION WITH A CLASS OF SKEWED t LINK MODELS

ABSTRACT

In this paper we propose a class of skewed t link models for analyzing binary response data with covariates. It is a class of asymmetric link models designed to improve the overall fit when commonly used symmetric links, such as the logit and probit links, do not provide the best fit available for a given binary response dataset. Introducing a skewed t distribution for the underlying latent variable, we develop the class of models. For the analysis of the models, a Bayesian and non-Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modelling and computation are provided. Finally, a simulation study and a real data example are used to illustrate the proposed methodology.