Prediction under uncertainty of degree of polynomial in growth curve models

Random coefficient polynomial regression model has been considered for prediction purpose when there is uncertainty about the degree of the polynomialo Expressions for mean square errors of two predictors based on simple estimators have been derived and their perfomaiices have been compared when parameters are estimated from the sample. A modified predictor has also been suggested when parameters in the predicting equations are to be estimated from the sample. Perform-ance ofseveral predictors haife been compared by cross validation technique from a real set of data.     

Admissible variable-selection procedures when fitting misspecified regression models by least squares

For loss equal to squared error of prediction, Kempthorne(l984) has proved that all variable-selection procedures are admissible for choosing among least-squares fits of a normal linear regression model. We extend this result to the case of a normal linear regression model in which the form of the expected response vector is misspecified.     

An empirical bayes approach to estimating the probability of correct selection

In the problem of selecting the best of k populations, Olkin, Sobel, and Tong (1976) have introduced the idea of estimating the probability of correct selection. In an attempt to improve on their estimator we consider anempirical Bayes approach. We compare the two estimators via analytic results and a simulation study.     

A subset selection procedure for multinomial distributions

A subset selection procedure is developed for selecting a subset containing the multinomial population that has the highest value of a certain linear combination of the multinomial cell probabilities; such population is called the ‘best’. The multivariate normal large sample approximation to the multinomial distribution is used to derive expressions for the probability of a correct selection, and for the threshold constant involved in the procedure. The procedure guarantees that the probability of a correct selection is at least at a pre-assigned level. The proposed procedure is an extension of Gupta and Sobel's [14] selection procedure for binomials and of Bakir's [2] restrictive selection procedure for multinomials. One illustration of the procedure concerns population income mobility in four countries: Peru, Russia, South Africa and the USA. Analysis indicates that Russia and Peru fall in the selected subset containing the best population with respect to income mobility from poverty to a higher-income status. The procedure is also applied to data concerning grade distribution for students in a certain freshman class.     

中小企业发展中的政府作为：发达国家的经验与我国的选择

转轨经济中中小企业在我国国民经济发展中发挥着越来越重要的作用，但在目前状况下我国中小企业还缺乏长足发展的优良环境。实际上发达国家中小企业的发展过程中也曾遇到我国目前正在经历的问题，但在这些国家通过制度建设得到了相应的妥善安排，日本、美国、德国、英国、俄国、韩国就是这些国家中的典型，这些国家通过国家立法、政府出面的信用担保、活化有利于中小企业的金融市场等手段给中小企业的发展创造了很好的宏观经济环境，其中的很多成功做法很值得学习和借鉴。     

黑城出土元代签补站户文书F116：W543考释

元代蒙古政权一贯把立站作为维护统治必不可少的手段，站户即政府签发来承担站役的人户，是站赤的核心力量，另立户籍。因站役繁重、给驿泛滥、自然灾害等因素以致站户生活疾苦，无力应役，大量逃亡或消乏，政府须及时签补站户，保证站赤功能的正常发挥。黑城出土了大量站赤文书，其中《签补站户文卷》中F116：W543记载元末站户消乏逃亡现象，朝廷命令各地站官及时签补站户，严格依照事产、物力、牲畜等财产标准来签补。上户、中户、下户一概签充，打破了等级限制，并招诱逃亡或迁移站户复业，以补充人数。他们一经签发，就成了站户主体，便长年累月、世世代代被束缚在驿站系统中，为站赤服务。     

Bayesian Additive Machine: classification with a semiparametric discriminant function

In this paper, we propose a new Bayesian inference approach for classification based on the traditional hinge loss used for classical support vector machines, which we call the Bayesian Additive Machine (BAM). Unlike existing approaches, the new model has a semiparametric discriminant function where some feature effects are nonlinear and others are linear. This separation of features is achieved automatically during model fitting without user pre-specification. Following the literature on sparse regression of high-dimensional models, we can also identify the irrelevant features. By introducing spike-and-slab priors using two sets of indicator variables, these multiple goals are achieved simultaneously and automatically, without any parameter tuning such as cross-validation. An efficient partially collapsed Markov chain Monte Carlo algorithm is developed for posterior exploration based on a data augmentation scheme for the hinge loss. Our simulations and three real data examples demonstrate that the new approach is a strong competitor to some approaches that were proposed recently for dealing with challenging classification examples with high dimensionality.     

Choosing Between the Sample-Selection Model and the Multi-Part Model

Hay and Olsen (1984) incorrectly argue that a multi-part model, the two-part model used in Duan et al. (1982,1983), is nested within the sample-selection model. Their proof relies on an unmentioned restrictive assumption that cannot be satisfied. We provide a counterexample to show that the propensity to use medical care and the level of expense can be positively associated in the two-part model, contrary to their assertion. The conditional specification in the multi-part model is preferable to the unconditional specification in the selection model for modeling actual (v. potential) outcomes. The selection model also has poor statistical and numerical properties and relies on untestable assumptions. Empirically the multi-part estimators perform as well as or better than the sample selection estimator for the data set analyzed in Duan et al. (1982, 1983).     

The Coverage Probability of Confidence Intervals in One‐Way Analysis of Covariance after Two F Tests

Volume 3 of Analysis of Messy Data by Milliken & Johnson (2002) provides detailed recommendations about sequential model development for the analysis of covariance. In his review of this volume, Koehler (2002) asks whether users should be concerned about the effect of this sequential model development on the coverage probabilities of confidence intervals for comparing treatments. We present a general methodology for the examination of these coverage probabilities in the context of the two‐stage model selection procedure that uses two F tests and is proposed in Chapter 2 of Milliken & Johnson (2002). We apply this methodology to an illustrative example from this volume and show that these coverage probabilities are typically very far below nominal. Our conclusion is that users should be very concerned about the coverage probabilities of confidence intervals for comparing treatments constructed after this two‐stage model selection procedure.     

Exploring Moderating Effects of Violent Conflict on Household Welfare in a Resource-Constrained Environment: The Case of Sub-Saharan Africa

ABSTRACT

This study examines correlates of household welfare in three countries of sub-Saharan Africa, Tanzania, Zambia, and Zimbabwe, using data from the 2004 wave of the Afrobarometer survey. We also assess the role violent conflict might play in influencing this relationship. This subject has become especially relevant today in sub-Saharan Africa, given the growing disfranchisement of vulnerable individuals and households and increased incidents of violent conflict. Insight into the relationship between violent conflict and household welfare may be an important step in understanding why many sub-Saharan African countries have difficulties in stimulating economic growth and welfare. The study sample includes 3,525 respondents. Study findings provide partial support for the hypothesized relationship. Specifically, poverty reduction initiatives and informal assistance are associated with reduced hardship, while violent conflict is related to an increase in hardship. We also note that certain individual and household characteristics are linked to hardship. Practice and policy implications are suggested.