Attention and Executive Functions as Mediators of Attachment and Behavior Problems

The purpose of this study was to provide a more comprehensive understanding of the relation between early parent‐child interactions and subsequent behavior problems and how certain cognitive processes mediate this relation. Specifically, this study investigated whether attention, inhibition, and planning skills mediate the relation between attachment security and behavior problems. Data were collected as part of the National Institute of Child Health and Development‐Study of Early Child Care and Youth Development when children (N = 1004) were between 36 months and the third grade. Results from structural equation models indicated that sustained attention mediated the relation between disorganized attachment and social problems. Planning mediated the relation between disorganized attachment and subsequent thought problems, attention problems, and delinquent behavior. Avoidant, ambivalent, and disorganized attachment directly predicted several behavior problems. Implications for theory and future research are discussed.     

论分享价值原则的文化战略学意义--十八届三中全会《决定》的理论解读

在“五位一体”框架中，探讨支撑国家整体发展战略的基本价值原则，乃是当代中国化马克思主义文化战略学的重要课题之一。中共十八届三中全会《决定》强调“让一切劳动、知识、技术、管理、资本的活力竞相迸发”，要做到这一点，在市场之内的初次分配中，必须让“资本-劳动”分享利润，与此相对应的是一种与“平等”等密切相关的“分享”价值原则。《决定》有关“多渠道增加居民财产性收入”“保障农民公平分享土地增值收益”等重大举措，同样是在市场之内贯彻分享价值原则，而以民生为主的社会建设、以公共服务为主的文化事业则是在市场之外推行分享价值原则的主要方式。全面推行分享价值原则，既有利于坚定地推进新型市场化改革，同时也有利于保障平等、促进和谐、拓展自由，对内可以凝聚社会共识，激发国民创造力，对外则有助于在价值制高点上展现社会主义中国的文化魅力和感召力。     

A procedure for testing the equality of k dependent correlation coefficients

A procedure is proposed for testing the equality of k dependent correlation coefficients. The procedure is simulated utilizing Monte Carlo techniques; and, a method for post hoc probing is also suggested.     

基于长沙市T区社区管理模式现状的思考

以长沙市T区为分析案例,通过对T区进行调查研究,寻找当前长沙市社区管理模式存在的一些问题:管理体制的行政化,政府职能的错位与缺位,居民与社区中介组织参与社区管理程度低,社区工作任务繁重,等等。要解决当前社区管理存在的一系列问题,必须重新考析政府、社区、社区中介组织以及居民之间的关系,理顺社区管理体制,明确政府职能,大力培育社区中介组织,培养居民的参与意识,构建长沙市功能型社区管理模式。     

On Modeling Wood Formation Using Parametric and Semiparametric Regressions for Count Data

Understanding how wood develops has become an important problematic of plant sciences. However, studying wood formation requires the acquisition of count data difficult to interpret. Here, the annual wood formation dynamics of a conifer tree species were modeled using generalized linear and additive models (GLM and GAM); GAM for location, scale, and shape (GAMLSS); a discrete semiparametric kernel regression for count data. The performance of models is evaluated using bootstrap methods. GLM was useful to describe the wood formation general pattern but had a lack of fitting, while GAM, GAMLSS, and kernel regression had a higher sensibility to short-term variations.     

Least squares estimation of varying‐coefficient hazard regression with application to breast cancer dose‐intensity data

To enhance modeling flexibility, the authors propose a nonparametric hazard regression model, for which the ordinary and weighted least squares estimation and inference procedures are studied. The proposed model does not assume any parametric specifications on the covariate effects, which is suitable for exploring the nonlinear interactions between covariates, time and some exposure variable. The authors propose the local ordinary and weighted least squares estimators for the varying‐coefficient functions and establish the corresponding asymptotic normality properties. Simulation studies are conducted to empirically examine the finite‐sample performance of the new methods, and a real data example from a recent breast cancer study is used as an illustration. The Canadian Journal of Statistics 37: 659–674; 2009 © 2009 Statistical Society of Canada     

Estimation of Nonparametric Models With Simultaneity

We introduce methods for estimating nonparametric, nonadditive models with simultaneity. The methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables, such as ratios of derivatives or averages of products of derivatives of this density. The estimators are therefore easily computed functionals of a nonparametric estimator of the density of the observable variables. We consider in detail a model where to each structural equation there corresponds an exclusive regressor and a model with one equation of interest and one instrument that is included in a second equation. For both models, we provide new characterizations of observational equivalence on a set, in terms of the density of the observable variables and derivatives of the structural functions. Based on those characterizations, we develop two estimation methods. In the first method, the estimators of the structural derivatives are calculated by a simple matrix inversion and matrix multiplication, analogous to a standard least squares estimator, but with the elements of the matrices being averages of products of derivatives of nonparametric density estimators. In the second method, the estimators of the structural derivatives are calculated in two steps. In a first step, values of the instrument are found at which the density of the observable variables satisfies some properties. In the second step, the estimators are calculated directly from the values of derivatives of the density of the observable variables evaluated at the found values of the instrument. We show that both pointwise estimators are consistent and asymptotically normal.     

Penalized proportion estimation for non parametric mixture of regressions

Abstract

In this article, we propose a penalized local log-likelihood method to locally select the number of components in non parametric finite mixture of regression models via proportion shrinkage method. Mean functions and variance functions are estimated simultaneously. We show that the number of components can be estimated consistently, and further establish asymptotic normality of functional estimates. We use a modified EM algorithm to estimate the unknown functions. Simulations are conducted to demonstrate the performance of the proposed method. We illustrate our method via an empirical analysis of the housing price index data of United States.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.