Variable selection approach for zero-inflated count data via adaptive lasso

This article proposes a variable selection approach for zero-inflated count data analysis based on the adaptive lasso technique. Two models including the zero-inflated Poisson and the zero-inflated negative binomial are investigated. An efficient algorithm is used to minimize the penalized log-likelihood function in an approximate manner. Both the generalized cross-validation and Bayesian information criterion procedures are employed to determine the optimal tuning parameter, and a consistent sandwich formula of standard errors for nonzero estimates is given based on local quadratic approximation. We evaluate the performance of the proposed adaptive lasso approach through extensive simulation studies, and apply it to analyze real-life data about doctor visits.     

试论流行短语的生成渠道及其语用效能

社会的发展变化必然引起词汇的变化，而词汇的变异常常带有明显的时代特征，它记录着人类生活的发展变化和观念的递嬗更新。改革开放以来，流行词汇大量涌现，成为人们生活中一道亮丽的风景线。为了比较全面地认识流行词汇中的流行短语，本主要从语用的角度，考察分析了流行短语的五种产生渠道，并从内部构成的修辞性理据和运用中的修辞性效能两个方面描写和阐释了流行短语的语用效能。     

VTR走带系统的齐次坐标变换

提出了一种分析磁带录像机(VTR)螺旋扫描走带系统设计参数、坐标计算及描述误差传递的数学方法.这种方法系统性强并有利于编程计算,已应用于本校录像系统生产技术研究院机电一体化研究室的研究工作,是空间复杂问题分析的一种有效方法.     

国有企业改掉后法人治理结构的角色辨析

本文利用角色分析方法 ,分析改制后国有企业法人治理结构各系统要素的角色行为 ,试图明确现存的上级党组织、政府、政府出资人代表或机构、企业、企业党委、企业工会和职工代表大会之间的关系 ,澄清它们各自的角色及其行为 ,形成各司其职、各负其责、有效制衡、协调运转的公司法人治理结构     

“所”与“所”字短语析疑

“所”是一个文言虚词,主用于书面语。学界对“所”的语法功能认识存有分歧,这些分歧明显反映在大学现代汉语教材当中。文章比较各家说法,分析“所”的语法功能,区分带“所”的不同结构,并结合不同分析模式加以说明,以进一步明晰对“所”的认识。     

论合同解除权与减损规则之间的矛盾协调

解除权之有无和行使与否与减损措施存在诸多联系和矛盾,二者应当在减损措施合理性标准的框架内实现二者的协调。在非违约方没有解除权时,其减损措施也不得否定整个合同;非违约方享有解除权时,解除权的行使与否不是非违约方为减损行为的前提,积极行使解除权还是要求违约方继续履行要依据事实情况而定。替代安排作为减损措施的一种也不以解除合同为条件,但应当符合"合理措施"之要求。     

基于词性信息自动识别和标注非分句

在完成自动分词和词性标注工作的基础上,进行分句层次和关系的自动划分和标注,以期建设一个面向中文信息处理的大规模复句"精加工"语料库.可以利用词性信息制定一系列规则去实现部分非分句的自动识别和标注,同时建设一个短语库,把短语语言片段收录其中.     

也谈汉语双音谓词的重叠

用语义句法理论和“相似/相关”、“向心/离心”、“离散/连续”、“有定/无定”等概念以及“话题—说明”的句子框架,分析汉语中与双音谓词（包括形容词和动词）的AABB式和ABAB式重叠有关的语言现象,从中归纳出6种不同的重叠类型,分析各种类型的语义特征和句法功能,以及不同重叠类型之间的异同和相互关系。     

Laplace Error Penalty‐based Variable Selection in High Dimension

We propose the Laplace Error Penalty (LEP) function for variable selection in high‐dimensional regression. Unlike penalty functions using piecewise splines construction, the LEP is constructed as an exponential function with two tuning parameters and is infinitely differentiable everywhere except at the origin. With this construction, the LEP‐based procedure acquires extra flexibility in variable selection, admits a unified derivative formula in optimization and is able to approximate the L₀ penalty as close as possible. We show that the LEP procedure can identify relevant predictors in exponentially high‐dimensional regression with normal errors. We also establish the oracle property for the LEP estimator. Although not being convex, the LEP yields a convex penalized least squares function under mild conditions if p is no greater than n. A coordinate descent majorization‐minimization algorithm is introduced to implement the LEP procedure. In simulations and a real data analysis, the LEP methodology performs favorably among competitive procedures.     

Empirical Likelihood for Censored Linear Regression and Variable Selection

The linear regression model for right censored data, also known as the accelerated failure time model using the logarithm of survival time as the response variable, is a useful alternative to the Cox proportional hazards model. Empirical likelihood as a non‐parametric approach has been demonstrated to have many desirable merits thanks to its robustness against model misspecification. However, the linear regression model with right censored data cannot directly benefit from the empirical likelihood for inferences mainly because of dependent elements in estimating equations of the conventional approach. In this paper, we propose an empirical likelihood approach with a new estimating equation for linear regression with right censored data. A nested coordinate algorithm with majorization is used for solving the optimization problems with non‐differentiable objective function. We show that the Wilks' theorem holds for the new empirical likelihood. We also consider the variable selection problem with empirical likelihood when the number of predictors can be large. Because the new estimating equation is non‐differentiable, a quadratic approximation is applied to study the asymptotic properties of penalized empirical likelihood. We prove the oracle properties and evaluate the properties with simulated data. We apply our method to a Surveillance, Epidemiology, and End Results small intestine cancer dataset.