“以学生为中心的主题教学模式”实验研究

通过“以学生为中心的主题教学模式”教改实验测试成绩数据和一系列调查结果的比较分析 ,说明了教学改革对于大学英语课堂教学的效果和学生学习能力的培养有着十分重要的作用     

Covariate Order Tests for Covariate Effect

A new approach for constructing tests for association between a random right censored life time variable and a covariate is proposed. The basic idea is to first arrange the observations in increasing order of the covariate and then base the test on a certain point process defined by the observation times. Tests constructed by this approach are robust against outliers in the covariate values or misspecification of the covariate scale since they only use the ordering of the covariate. Of particular interest is a test based on the Anderson-Darling statistic. This test has good power properties both against monotonic and nonmonotonic dependencies between the covariate and the life time variable.     

技术路径与中国经济增长

中国在改革开放进程中所采取的积极的产业政策拓展了与发达国家或地区的贸易关系,加快了外资引进的步伐。对外贸易和外资引进产生了技术的"外溢"和"干中学"效应,提高了市场的竞争程度,也促使国内企业积极去改进原有技术和开发新技术。这三个技术进步路径共同推动了企业技术的进步,促进了中国经济的增长。     

国外关于社区对儿童和青少年影响的研究

国外关于社区 (neighborhood)对儿童和青少年影响的研究已有多年的历史并取得了不少成果。社区对儿童和青少年的影响效应一般从入学准备情况与学业成绩、行为和情绪问题 (包括外化的和内化的 )、性行为与生育等三个方面来研究考察。社区的结构维度大致可划分收入 /社会经济地位 (可划分为高、中、低三类 ) ,种族混杂程度 ,居民搬迁率等三个。社区对儿童和青少年的影响机制一般从机构资源、亲子关系和组织度三个角度来探讨     

A boundary kernel for local polynomial regression

We define and compute a boundary kernel for local polynomial regression, We prove that the new kernel provides improvement over the existing kernels, Simulations show the improvement in finite samples.     

Physical exercise,employee fitness and the management of health at work

Abstract

This paper presents some recent data on why organizations invest in exercise and physical fitness programmes for their stiff. These data suggest that, owing to a lack of evaluation studies, organizations act mainly on the basis of assumption and belief. However, from a subsequent review of the available literature on the personal and organizational effects of such programmes, it appears that these assumptions and beliefs are not altogether unfounded. The paper concludes by discussing the ways in which exercise and employee fitness programmes might help die individual and their organization in terms of the management of health at work.     

Second-Order Response Surface Model with Neighbor Effects

This article considers the second-order response surface model in which the experimental units, i.e., plots experience the neighbor effects from immediate left and right neighboring plots assuming the plots to be placed adjacent linearly with no gaps. Conditions have been derived for the estimation of coefficients of second-order response surface model. A method of constructing designs for fitting second-order response surface in the presence of neighbor effects has been developed. The designs so obtained are found to be rotatable.     

Penalized spline joint models for longitudinal and time-to-event data

The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models.     

Product Introduction Strategies under Sequential Innovation for Durable Goods with Network Effects

When network effects are important and technology is rapidly improved, this study explores the relative optimality of five product introduction strategies of a durable goods manufacturer: (1) replacement, (2) skipping, (3) a delayed line, (4) shelving, and (5) line‐extension. Using a two‐period analytical model, we show how the type of compatibility—either full or backward compatibility—and the magnitude of the network effect influence the manufacturer's preference for the above strategies. Our analysis reveals that only the strategies (1)–(3) above can be optimal; and the optimal strategy varies with network strength. Further, the type of compatibility can dramatically change the profitability under each optimal strategy; for instance, while backward compatibility can increase the profitability of replacement under certain conditions, it always reduces the profitability of a delayed line. We also illustrate that if compatibility were a choice, although backward compatibility may be observed widely in practice, the parametric region for its optimality is relatively more restricted than that of full compatibility.     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.