外来人口与吴江发展

通过对吴江市外来人口的现状及其在吴江社会经济发展中的地位和作用的考察,分析外来人口对吴江可持续发展的影响程度,指出目前吴江流动人口管理和服务中的薄弱环节,并在如何适度调控外来人口、加强人口规划、增强外来人口的服务和管理、减少管理环节、创新社区化管理服务等方面,提出一些建议.     

钢琴养护与调律

“乐器之王”——钢琴由近万个零部件组成,其设计之科学、结构之复杂、工艺之精密居所有乐器之首。由于气候和地域的差别以及钢琴使用程度等原因有必要对钢琴实施养护。而钢琴调律工作在音乐学习、音乐创作和音乐实践中起着重要的作用。只有钢琴的音律准确,琴弦的张力达到了钢琴的设计要求,琴弦获得音板等部件良好的共振,钢琴才能发出准确而优美动听的声音。     

Tuning Parameter Estimation in Penalized Least Squares Methodology

The efficiency of the penalized methods (Fan and Li, 2001 Fan , J. , Li , R. ( 2001 ). Variable selection via nonconcave penalized likelihood and its oracle properties . Journal of the American Statistical Association 96 : 1348 – 1360 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) depends strongly on a tuning parameter due to the fact that it controls the extent of penalization. Therefore, it is important to select it appropriately. In general, tuning parameters are chosen by data-driven approaches, such as the commonly used generalized cross validation. In this article, we propose an alternative method for the derivation of the tuning parameter selector in penalized least squares framework, which can lead to an ameliorated estimate. Simulation studies are presented to support theoretical findings and a comparison of the Type I and Type II error rates, considering the L ₁, the hard thresholding and the Smoothly Clipped Absolute Deviation penalty functions, is performed. The results are given in tables and discussion follows.     

Evaluating the performance of sparse principal component analysis methods in high-dimensional data scenarios

High-dimensional datasets have exploded into many fields of research, challenging our interpretation of the classic dimension reduction technique, Principal Component Analysis (PCA). Recently proposed Sparse PCA methods offer useful insight into understanding complex data structures. This article compares three Sparse PCA methods through extensive simulations, with the aim of providing guidelines as to which method to choose under a variety of data structures, as dictated by the variance-covariance matrix. A real gene expression dataset is used to illustrate an application of Sparse PCA in practice and show how to link simulation results with real-world problems.     

Tuning Parameter Selector for the Penalized Likelihood Method in Multivariate Generalized Linear Models

Variable selection is fundamental to high-dimensional multivariate generalized linear models. The smoothly clipped absolute deviation (SCAD) method can solve the problem of variable selection and estimation. The choice of the tuning parameter in the SCAD method is critical, which controls the complexity of the selected model. This article proposes a criterion to select the tuning parameter for the SCAD method in multivariate generalized linear models, which is shown to be able to identify the true model consistently. Simulation studies are conducted to support theoretical findings, and two real data analysis are given to illustrate the proposed method.     

An efficient model-free estimation of multiclass conditional probability

Conventional multiclass conditional probability estimation methods, such as Fisher's discriminate analysis and logistic regression, often require restrictive distributional model assumption. In this paper, a model-free estimation method is proposed to estimate multiclass conditional probability through a series of conditional quantile regression functions. Specifically, the conditional class probability is formulated as a difference of corresponding cumulative distribution functions, where the cumulative distribution functions can be converted from the estimated conditional quantile regression functions. The proposed estimation method is also efficient as its computation cost does not increase exponentially with the number of classes. The theoretical and numerical studies demonstrate that the proposed estimation method is highly competitive against the existing competitors, especially when the number of classes is relatively large.     

Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection

This article develops the adaptive elastic net generalized method of moments (GMM) estimator in large-dimensional models with potentially (locally) invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the adaptively weighted lasso shrinkage and the quadratic regularization. It is a one-step procedure of valid moment condition selection, nonzero structural parameter selection (i.e., model selection), and consistent estimation of the nonzero parameters. The procedure achieves the standard GMM efficiency bound as if we know the valid moment conditions ex ante, for which the quadratic regularization is important. We also study the tuning parameter choice, with which we show that selection consistency still holds without assuming Gaussianity. We apply the new estimation procedure to dynamic panel data models, where both the time and cross-section dimensions are large. The new estimator is robust to possible serial correlations in the regression error terms.     

Tuning Parameter Selection in Penalized Frailty Models

The penalized likelihood approach of Fan and Li (2001 Fan, J., Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Association 96:1348–1360.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], 2002 Fan, J., Li, R. (2002). Variable selection for Cox’s proportional hazards model and frailty model. The Annals of Statistics 30:74–99.[Crossref], [Web of Science ®] , [Google Scholar]) differs from the traditional variable selection procedures in that it deletes the non-significant variables by estimating their coefficients as zero. Nevertheless, the desirable performance of this shrinkage methodology relies heavily on an appropriate selection of the tuning parameter which is involved in the penalty functions. In this work, new estimates of the norm of the error are firstly proposed through the use of Kantorovich inequalities and, subsequently, applied to the frailty models framework. These estimates are used in order to derive a tuning parameter selection procedure for penalized frailty models and clustered data. In contrast with the standard methods, the proposed approach does not depend on resampling and therefore results in a considerable gain in computational time. Moreover, it produces improved results. Simulation studies are presented to support theoretical findings and two real medical data sets are analyzed.     

“欧洲教育结构调整”项目中教师教育标准的内容及特征

为实现欧洲各国教师教育的共通性,促进教师在欧洲各国的流动,“欧洲教育结构调整”项目制定了教师教育标准,对教师教育的学位资格框架、能力标准、学分转换制度和学习、教学、评价方法及质量保障与提高进行了详细阐述。教师教育标准彰显出面向利益相关者的综合化价值取向、以能力为导向以及一致性与多样性相结合的特征,对欧洲各国教师教育课程设置与教学改革具有导向作用。