Modeling cooperation among self-interested agents: a critique

This paper reviews recent attempts to model cooperation in groups of self-interested unrelated individuals. It concludes that no empirically plausible model has been developed, and the economist's quest for an explanation of human prosociality in terms of self-interested agents has not been successful. We show that the equilibrium criteria employed in standard game-theoretic models have no attractive dynamic properties, and the mechanisms deployed in these models are not generally seen in empirical examples of cooperative groups. We also show that these models are not robust to plausible rates of signaling error and mutation. We present an alternative, involving strongly reciprocal agents, that satisfies all the requirements for a dynamically stable model of cooperation.     

《洛阳伽蓝记》里的单纯词

本文具体分类研究了《洛阳伽蓝记》中各种类型的单纯词,简要概括了单纯词在汉语词汇史上的地位。     

Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Summary.  In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.     

Influence diagnostics and outlier tests for semiparametric mixed models

Summary. Semiparametric mixed models are useful in biometric and econometric applications, especially for longitudinal data. Maximum penalized likelihood estimators (MPLEs) have been shown to work well by Zhang and co-workers for both linear coefficients and nonparametric functions. This paper considers the role of influence diagnostics in the MPLE by extending the case deletion and subject deletion analysis of linear models to accommodate the inclusion of a nonparametric component. We focus on influence measures for the fixed effects and provide formulae that are analogous to those for simpler models and readily computable with the MPLE algorithm. We also establish an equivalence between the case or subject deletion model and a mean shift outlier model from which we derive tests for outliers. The influence diagnostics proposed are illustrated through a longitudinal hormone study on progesterone and a simulated example.     

Bootstrap methods for bias correction and confidence interval estimation for nonlinear quantile regression of longitudinal data

This paper examines the use of bootstrapping for bias correction and calculation of confidence intervals (CIs) for a weighted nonlinear quantile regression estimator adjusted to the case of longitudinal data. Different weights and types of CIs are used and compared by computer simulation using a logistic growth function and error terms following an AR(1) model. The results indicate that bias correction reduces the bias of a point estimator but fails for CI calculations. A bootstrap percentile method and a normal approximation method perform well for two weights when used without bias correction. Taking both coverage and lengths of CIs into consideration, a non-bias-corrected percentile method with an unweighted estimator performs best.     

Some new constructions of circular balanced repeated measurements designs

Repeated measurements designs are widely used in medicine, pharmacology, animal sciences, and psychology. In this article, some infinite series are developed to generate the balanced repeated measurements designs for p (periods) even. For p odd, construction procedures are also described. Catalogues of the proposed designs are also presented for p = 5, 7, 9, when v ≤ 100.     

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered – e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion.     

Comparison of methods for analyzing binary repeated measures data: A simulation-based study (comparison of methods for binary repeated measures)

In this study, some methods suggested for binary repeated measures, namely, Weighted Least Squares (WLS), Generalized Estimating Equations (GEE), and Generalized Linear Mixed Models (GLMM) are compared with respect to power, type 1 error, and properties of estimates. The results indicate that with adequate sample size, no missing data, the only covariate being time effect, and a relatively limited number of time points, the WLS method performs well. The GEE approach performs well only for large sample sizes. The GLMM method is satisfactory with respect to type I error, but its estimates have poorer properties than the other methods.     

福费廷服务的法律思考

福费廷是一种新兴的以无追索权为特点的国际融资服务项目。西方国家的绝大多数大银行都开展了此项业务,而我国银行和贸易商对此了解不多。中国在入世时承诺的五年过渡期即将结束,外资银行纷纷抢滩中国市场,凭借其丰富的管理经验和优质多样的服务品种积极争夺中国国内的优质客户,南京爱立信"倒戈"花旗银行便是例证。中国的银行业面临前所未有的严峻局面。福费廷的研究和利用能使中资银行在竞争中获得一席之地,更好地迎接挑战。     

吉林省土地征收中农民权利探析

随着吉林省经济的迅速发展和农村城市化进程的加快,对农村土地的征收越来越多,越来越频繁。土地征收既要最大限度保护农民权利,同时也应适当结合平衡原则加以考虑。但是,由于我国现阶段土地征收制度不健全,侵犯农民权利的现象屡有发生,对吉林省农村的稳定产生了消极影响。通过深入分析吉林省土地征收中农民权利现状,提出完善现行土地征收法律制度的对策。