Simultaneous variable and factor selection via sparse group lasso in factor analysis

This paper considers variable and factor selection in factor analysis. We treat the factor loadings for each observable variable as a group, and introduce a weighted sparse group lasso penalty to the complete log-likelihood. The proposal simultaneously selects observable variables and latent factors of a factor analysis model in a data-driven fashion; it produces a more flexible and sparse factor loading structure than existing methods. For parameter estimation, we derive an expectation-maximization algorithm that optimizes the penalized log-likelihood. The tuning parameters of the procedure are selected by a likelihood cross-validation criterion that yields satisfactory results in various simulation settings. Simulation results reveal that the proposed method can better identify the possibly sparse structure of the true factor loading matrix with higher estimation accuracy than existing methods. A real data example is also presented to demonstrate its performance in practice.     

Modeling association between multivariate correlated outcomes and high-dimensional sparse covariates: the adaptive SVS method

The problem of modeling the relationship between a set of covariates and a multivariate response with correlated components often arises in many areas of research such as genetics, psychometrics, signal processing. In the linear regression framework, such task can be addressed using a number of existing methods. In the high-dimensional sparse setting, most of these methods rely on the idea of penalization in order to efficiently estimate the regression matrix. Examples of such methods include the lasso, the group lasso, the adaptive group lasso or the simultaneous variable selection (SVS) method. Crucially, a suitably chosen penalty also allows for an efficient exploitation of the correlation structure within the multivariate response. In this paper we introduce a novel variant of such method called the adaptive SVS, which is closely linked with the adaptive group lasso. Via a simulation study we investigate its performance in the high-dimensional sparse regression setting. We provide a comparison with a number of other popular methods under different scenarios and show that the adaptive SVS is a powerful tool for efficient recovery of signal in such setting. The methods are applied to genetic data.     

Mean estimate in ranked set sampling using a length-biased concomitant variable

In this paper, a ranked set sampling procedure with ranking based on a length-biased concomitant variable is proposed. The estimate for population mean based on this sample is given. It is proved that the estimate based on ranked set samples is asymptotically more efficient than the estimate based on simple random samples. Simulation studies are conducted to present the properties of the proposed estimate for finite sample size. Moreover, the consequence of ignoring length bias is also addressed by simulation studies and the real data analysis.     

Inference on local average treatment effects for misclassified treatment

We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is nonclassical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.     

Inferences on a linear combination of K multivariate normal mean vectors

In this paper, the hypothesis testing and confidence region construction for a linear combination of mean vectors for K independent multivariate normal populations are considered. A new generalized pivotal quantity and a new generalized test variable are derived based on the concepts of generalized p-values and generalized confidence regions. When only two populations are considered, our results are equivalent to those proposed by Gamage et al. [Generalized p-values and confidence regions for the multivariate Behrens–Fisher problem and MANOVA, J. Multivariate Aanal. 88 (2004), pp. 117–189] in the bivariate case, which is also known as the bivariate Behrens–Fisher problem. However, in some higher dimension cases, these two results are quite different. The generalized confidence region is illustrated with two numerical examples and the merits of the proposed method are numerically compared with those of the existing methods with respect to their expected areas, coverage probabilities under different scenarios.     

Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects

When a linear model is adjusted to control for additional explanatory variables, the sign of a fitted coefficient may reverse. Here, these reversals are studied using coefficients of determination. The resulting theory can be used to determine directions of unique effects in the presence of model uncertainty. This process is called model-invariant estimation when the estimates are invariant across changes to the model structure. When a single covariate is added, the reversal region can be understood geometrically as an elliptical cone of two nappes with an axis of symmetry relating to a best-possible condition for a reversal using a single coefficient of determination. When a set of covariates are added to a model with a single explanatory variable, model-invariant estimation can be implemented using subject matter knowledge. More general theory with partial coefficients is applicable to analysis of large datasets. Applications are demonstrated with dietary health data from the United Nations.     

通信企业员工人口变量与心理契约关系研究

心理契约是企业和员工双方彼此对对方应该付出什么同时又应该得到什么的一种主观心理约定,是联系企业与员工的心理纽带,是企业对员工进行有效人力资源管理的重要工具。在企业组织结构调整、雇用关系改变的过程中,心理契约是最敏感、最集中反映这种变化的核心因素。在通信企业快速发展的今天,员工与企业之间的心理契约的变化严重影响着雇佣关系。以江苏的通信企业员工为研究对象,采用问卷调查的方式,并利用SPSS16.0统计软件包进行数据统计分析,对不同人口变量在心理契约上的不同感受进行探索性研究,研究结果验证了本课题的研究假设：不同个体变量在心理契约的感受上有显著差异。并结合研究成果,针对通信企业员工的心理契约管理提出一些建议。     

变化义名谓句的认知语义分析

变化义名谓句属于动态义名谓句,其谓语大都是“顺序义名词 了”的结构形式。变化义名谓句的变化主要是名词的语义值的变化,根据其语义值的变化度将变化义名谓句分为增值变化句、等值变化句、减值变化句和比值变化句四种类型。名词在谓语位置上表现的变化包括自然的变化和社会性的变化两种形式。变化总是在一定序列当中发生的,名词的顺序序列有三种:绝对顺序序列、相对顺序序列和临时顺序序列。变化是以参照点为背景的,每个名词所指的事物既以其他事物为背景,同时又是其他事物的背景。