Model-averaged ℓ1 regularization using Markov chain Monte Carlo model composition

Bayesian model averaging (BMA) is an effective technique for addressing model uncertainty in variable selection problems. However, current BMA approaches have computational difficulty dealing with data in which there are many more measurements (variables) than samples. This paper presents a method for combining ?₁ regularization and Markov chain Monte Carlo model composition techniques for BMA. By treating the ?₁ regularization path as a model space, we propose a method to resolve the model uncertainty issues arising in model averaging from solution path point selection. We show that this method is computationally and empirically effective for regression and classification in high-dimensional data sets. We apply our technique in simulations, as well as to some applications that arise in genomics.     

Investigation about a screening step in model selection

In many studies a large number of variables is measured and the identification of relevant variables influencing an outcome is an important task. For variable selection several procedures are available. However, focusing on one model only neglects that there usually exist other equally appropriate models. Bayesian or frequentist model averaging approaches have been proposed to improve the development of a predictor. With a larger number of variables (say more than ten variables) the resulting class of models can be very large. For Bayesian model averaging Occam’s window is a popular approach to reduce the model space. As this approach may not eliminate any variables, a variable screening step was proposed for a frequentist model averaging procedure. Based on the results of selected models in bootstrap samples, variables are eliminated before deriving a model averaging predictor. As a simple alternative screening procedure backward elimination can be used. Through two examples and by means of simulation we investigate some properties of the screening step. In the simulation study we consider situations with fifteen and 25 variables, respectively, of which seven have an influence on the outcome. With the screening step most of the uninfluential variables will be eliminated, but also some variables with a weak effect. Variable screening leads to more applicable models without eliminating models, which are more strongly supported by the data. Furthermore, we give recommendations for important parameters of the screening step.     

Relationship Preferences Among Gay and Lesbian Online Daters: Individual and Contextual Influences

There is currently little knowledge about what gay men and lesbians seek in a romantic relationship. This study extends the literature on gay men and lesbians' partnership preferences by engaging in the first large‐scale empirical study of the long‐term dating intentions and monogamy beliefs of gay and lesbian online daters across 53 regions in 8 European countries (N = 24,598). Looking at profile and preference information, the authors examined both individual and contextual determinants in a series of multilevel logistic regression analyses. They show that lesbians give more importance to monogamy but show less interest in starting a long‐term relationship. The data also reveal the importance of life course aspects such as relationship history and presence of children. Finally, the authors empirically demonstrate that social tolerance and legal recognition of same‐sex unions are associated with higher long‐term dating intentions and stronger monogamy beliefs.     

语篇隐喻的类别及体现规则

语法隐喻的体现规则是具有语义发生过程的级转移和范畴转移，语篇隐喻也应该遵循相应的体现规则。语篇隐喻包括主位隐喻、信息隐喻和衔接隐喻，主位隐喻和信息隐喻通过级转移得以体现，衔接隐喻通过语法手段和词汇手段之间以及显性连接和隐性连接之间的范畴转移得以体现。     

Proportional hazards models based on biased samples and estimated selection probabilities

In non‐randomized biomedical studies using the proportional hazards model, the data often constitute an unrepresentative sample of the underlying target population, which results in biased regression coefficients. The bias can be avoided by weighting included subjects by the inverse of their respective selection probabilities, as proposed by Horvitz & Thompson (1952) and extended to the proportional hazards setting for use in surveys by Binder (1992) and Lin (2000). In practice, the weights are often estimated and must be treated as such in order for the resulting inference to be accurate. The authors propose a two‐stage weighted proportional hazards model in which, at the first stage, weights are estimated through a logistic regression model fitted to a representative sample from the target population. At the second stage, a weighted Cox model is fitted to the biased sample. The authors propose estimators for the regression parameter and cumulative baseline hazard. They derive the asymptotic properties of the parameter estimators, accounting for the difference in the variance introduced by the randomness of the weights. They evaluate the accuracy of the asymptotic approximations in finite samples through simulation. They illustrate their approach in an analysis of renal transplant patients using data obtained from the Scientific Registry of Transplant Recipients     

On boosting kernel density methods for multivariate data: density estimation and classification

Statistical learning is emerging as a promising field where a number of algorithms from machine learning are interpreted as statistical methods and vice-versa. Due to good practical performance, boosting is one of the most studied machine learning techniques. We propose algorithms for multivariate density estimation and classification. They are generated by using the traditional kernel techniques as weak learners in boosting algorithms. Our algorithms take the form of multistep estimators, whose first step is a standard kernel method. Some strategies for bandwidth selection are also discussed with regard both to the standard kernel density classification problem, and to our 'boosted' kernel methods. Extensive experiments, using real and simulated data, show an encouraging practical relevance of the findings. Standard kernel methods are often outperformed by the first boosting iterations and in correspondence of several bandwidth values. In addition, the practical effectiveness of our classification algorithm is confirmed by a comparative study on two real datasets, the competitors being trees including AdaBoosting with trees.     

美国匿名消息源：使用规范与伦理选择

媒体从业者对匿名消息源的运用一直是美国新闻界探讨新闻伦理问题时一个无法回避的难题。由于复杂的原因，匿名消息源有时扮演天使传播正义的声音，有时又是幕后“魔鬼”制造事端，他们的出现使媒体从业者在实践过程中遭遇到诸多新闻职业道德与法律上的问题。媒体从业者做出的选择是试图通过制定相关的行为规范以正确运用匿名消息源。     

Hierarchical priors for Bayesian CART shrinkage

The Bayesian CART (classification and regression tree) approach proposed by Chipman, George and McCulloch (1998) entails putting a prior distribution on the set of all CART models and then using stochastic search to select a model. The main thrust of this paper is to propose a new class of hierarchical priors which enhance the potential of this Bayesian approach. These priors indicate a preference for smooth local mean structure, resulting in tree models which shrink predictions from adjacent terminal node towards each other. Past methods for tree shrinkage have searched for trees without shrinking, and applied shrinkage to the identified tree only after the search. By using hierarchical priors in the stochastic search, the proposed method searches for shrunk trees that fit well and improves the tree through shrinkage of predictions.     

我国公共部门人力资源管理的研究现状分析

公共部门人力资源管理研究在我国最近几年日益受到广大学者重视,研究角度多样化,研究成果较为丰富,相关理论得到巨大发展.本文运用中文社会科学引文索引数据库（CSSCI）进行检索,得到了近10年（2002-2012年）来与我国公共部门人力资源管理相关的78篇学术论文,从宏观和微观公共部门人力资源管理研究状况进行综述;在此基础上,分析评价了我国公共部门人力资源管理研究进程中呈现的特征,并指出了研究中的不足,希望对新时期我国公共部门人力资源管理改革提供参考.     

清代监察官员的选任、管理及对现代监察建设的启示

清代是中国封建社会的最后阶段，中国封建社会监察制度在这一时期达到完备。作者希望通过对这一 时期监察官员选任、管理制度的探讨，对当前的监察制度建设有所借鉴。本文由两部分组成：第一部分对清代的 监察官员的选任、管理制度进行了分析；第二部分作者借鉴历史对当今的监察制度建设提出了几点建议，认为应 保持监察系统的独立性，加强监察立法，严格对监察干部的选任、管理等等。