A novel regularization method for estimation and variable selection in multi-index models

Multi-index models have attracted much attention recently as an approach to circumvent the curse of dimensionality when modeling high-dimensional data. This paper proposes a novel regularization method, called MAVE-glasso, for simultaneous parameter estimation and variable selection in multi-index models. The advantages of the proposed method include transformation invariance, automatic variable selection, automatic removal of noninformative observations, and row-wise shrinkage. An efficient row-wise coordinate descent algorithm is proposed to calculate the estimates. Simulation and real examples are used to demonstrate the excellent performance of MAVE-glasso.     

企业重组、政府作用与市场秩序————对近年来国内几个钢企并购案的分析

政府与市场关系的本质问题是政府和市场两种秩序的问题。在经济全球化和金融危机的冲击下,国内钢企的重组以及经济转型的根本问题是社会主义市场经济转型中政府治理模式和政府角色的适时转型问题,其根本途径是通过推动政府治理模式从直接以行政手段干预经济运行转变到建立有限、责任、法治的现代政府,建立公平、正义的规则,完善产权制度、现代企业制度和政府投资项目责任追究制度,继续发挥政府的宏观调控作用,继续推进政府从一般市场竞争性领域中退出,克服行政垄断和地方保护主义,经由政府治理转型推动市场秩序的规范和完善。     

Combining estimates from multiple early studies to obtain estimates of response: using shrinkage estimates to obtain estimates of response

Development of anti-cancer therapies usually involve small to moderate size studies to provide initial estimates of response rates before initiating larger studies to better quantify response. These early trials often each contain a single tumor type, possibly using other stratification factors. Response rate for a given tumor type is routinely reported as the percentage of patients meeting a clinical criteria (e.g. tumor shrinkage), without any regard to response in the other studies. These estimates (called maximum likelihood estimates or MLEs) on average approximate the true value, but have variances that are usually large, especially for small to moderate size studies. The approach presented here is offered as a way to improve overall estimation of response rates when several small trials are considered by reducing the total uncertainty.The shrinkage estimators considered here (James-Stein/empirical Bayes and hierarchical Bayes) are alternatives that use information from all studies to provide potentially better estimates for each study. While these estimates introduce a small bias, they have a considerably smaller variance, and thus tend to be better in terms of total mean squared error. These procedures provide a better view of drug performance in that group of tumor types as a whole, as opposed to estimating each response rate individually without consideration of the others. In technical terms, the vector of estimated response rates is nearer the vector of true values, on average, than the vector of the usual unbiased MLEs applied to such trials.     

On the ridge regression estimator with sub-space restriction

In the linear regression model with elliptical errors, a shrinkage ridge estimator is proposed. In this regard, the restricted ridge regression estimator under sub-space restriction is improved by incorporating a general function which satisfies Taylor’s series expansion. Approximate quadratic risk function of the proposed shrinkage ridge estimator is evaluated in the elliptical regression model. A Monte Carlo simulation study and analysis based on a real data example are considered for performance analysis. It is evident from the numerical results that the shrinkage ridge estimator performs better than both unrestricted and restricted estimators in the multivariate t-regression model, for some specific cases.     

The Loss Rank Criterion for Variable Selection in Linear Regression Analysis

Abstract. Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected set. The approach leads to a fast and efficient procedure for variable selection, especially in high‐dimensional settings. Model selection consistency of the suggested criterion is proven when the number of covariates d is fixed. Simulation studies suggest that the criterion still enjoys model selection consistency when d is much larger than the sample size. The simulations also show that our approach for variable selection works surprisingly well in comparison with existing competitors. The method is also applied to a real data set.     

连续刚构桥合龙顶推力优化计算方法

预应力砼连续刚构桥因其施工简单、造价经济、受力合理、行车舒适等优势迅速发展,但成桥后混凝土收缩、徐变及温度变化将对连续刚构桥主梁和桥墩的变形及内力产生较大影响.结合州河大桥工程实例,充分考虑确定顶推力的主要因素,对连续刚构桥合龙前顶推量取值进行研究.结果表明,合理的顶推力能显著地改善桥墩的受力.     

骨架密实型水泥稳定碎石的配合比设计方法及路用性能研究

在总结国内外研究的基础上，提出骨架密实型水泥稳定碎石，用以减少基层收缩裂缝产生，从而提高水泥稳定碎石基层使用性能，同时与国内规范推荐级配的水泥稳定碎石作比较，结果表明，骨架密实型水泥稳定碎石具有优良的路用性能。     

高强混凝土收缩及补偿措施试验研究

在高强混凝土中掺加粉煤灰、膨胀剂,配置钢筋,再加强早期养护可有效减小高强混凝土的早期收缩.特别是在配有一定钢筋的混凝土中,可使混凝土处于一种预压状态,对防止混凝土早期收缩裂缝非常有利.     

Une condition nécessaire d'admissibilité et ses conséquences sur les estimateurs à rétrécisseur de la moyenne d'un vecteur normal

Considering exponential families of distributions, we estimate parameters which are not the natural parameters. We prove that the admissible estimators of these parameters are limits of Bayes estimators and can be expressed through a given functional form. An important particular case of this model pertains to the estimation of the mean of a multidimensional normal distribution when the variance is known up to a multiplicative factor. We deduce from the main result a necessry condition for the admissibility of matricial shrinkage estimators.