文化空缺视野下中美认知模式比较研究

成功的跨文化交际不仅要了解文化差异,更要了解文化空缺现象背后认知模式的差异及其根源。中美文化中存在大量的文化空缺现象,这也是双方认知模式迥异的根源。在对文化空缺进行理论概述的基础上,对中美文化空缺现象进行对比分析,深入探讨由此带来的中美认知模式差异。在文化空缺的基础上对中美认知模式进行比较研究,有助于中美跨文化交际顺利进行,促进中美关系健康发展。     

上海推进“城中村”改造研究

改造"城中村",有利于改善居民居住生活质量,促进城乡统筹发展;有利于盘活存量用地,推进土地的节约、集约利用;有利于改善城乡形象、推进宜居城市建设。上海进一步推进"城中村"改造,须加强领导,推进"城中村"改造的体制机制建设;要均衡各方利益,调动政府、开发商和农民三方的积极性;要摸清底数,对纳入全市统一改造计划内的"城中村"实行目录管理;要因地制宜,针对各个"城中村"不同的情况进行分类指导;要突破创新,建立、健全支持"城中村"改造的政策体系;要营造氛围,形成有利于推进"城中村"改造的良好外部环境。     

Estimating the effectiveness of a misdemeanor drug diversion program using propensity score matching and survival analyses

This study analyzes three years of data on misdemeanor drug offenders in Winnebago County, Wisconsin. A portion of these offenders opted into a Misdemeanor Drug Diversion Program (MDDP) offered instead of traditional adjudication. Recidivism in the treatment and comparison groups is estimated using standard binary response techniques augmented with propensity score matching to address selection bias. Results show that the MDDP reduces the probability of re-offense by 16%, after adjusting for possible selection bias. Cox proportional hazard modeling is also used to assess time-to-re-offense differentials between the treatment and comparison groups. The survival analysis indicates that the hazard rate of re-offense is 60% lower per day among those treated with the MDDP program than those who did not complete the program. The average number of days to re-offense among those that do re-offend is 297 days in the treatment group and 203 days in the comparison group.     

Keeping Surveys Valid,Reliable, and Useful: A Tutorial

This tutorial focuses on how to produce reliable and generalizable data from random‐digit‐dialing (RDD) landline and cell phone surveys. The article notes that RDD response rates have declined and explores the impact of this pronounced decline. The tutorial addresses order, response mode, and many other biases, sample size, cooperation and response rates, weighting, and hybrid designs‐all using examples from risk analysis to illustrate the key points. The article ends with a brief review of the advantages and disadvantages of major Internet and paper surveys tools, and how these can be molded and sometimes combined in repeated, longitudinal, and other designs to answer questions about risk preferences and perceptions.     

A new confidence interval in measurement error model with the reliability ratio known

For the slope parameter of the measurement error model with the reliability ratio known, this article constructs a fiducial generalized confidence interval (FGCI) which is proved to have correct asymptotic coverage. Simulation results demonstrate that the FGCI often outperforms the existing intervals in terms of empirical coverage probability, average interval length, and false parameter coverage rate. Two examples are also provided to illustrate our approach.     

Empirical Bayes estimation in continuous one-parameter exponential families under associated samples

In this paper, we study the empirical Bayes (EB) estimation in continuous one-parameter exponential families under negatively associated (NA) samples and positively associated (PA) samples. Under certain regularity conditions, it is shown that the convergence rates of proposed EB estimators under NA or PA samples are the same as those of EB estimators under independent observations, which significantly improve the existing results in EB estimation under associated samples.     

A general long-term aging model with different underlying activation mechanisms: Modeling,Bayesian estimation,and case influence diagnostics

In this paper we propose a general cure rate aging model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes of the event of interest is assumed to follow a logarithmic distribution. The model is parameterized in terms of the cured fraction which is then linked to covariates. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given, as well as case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as the Kullback–Leibler (K-L), J-distance, L₁ norm, and χ²-square divergence measures. Simulation studies are performed and experimental results are illustrated based on a real malignant melanoma data.     

Generalized Kerridge’s inaccuracy measure for conditionally specified models

Recently, conditional Renyi’s divergence of order α and Kerridge’s inaccuracy measures are studied by Navarro et al. (2014 Navarro, J., Sunoj, S.M., Linu, M.N. (2014). Characterizations of bivariate models using some dynamic conditional information divergence measures. Commun. Stat. Theory Methods 43:1939–1948.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). In the present article, a generalized dynamic conditional Kerridge’s inaccuracy measure is introduced, which can be represented as the sum of conditional Renyi’s divergence and Renyi’s entropy. Some useful bounds are obtained using the concept of likelihood ratio order. The results are extended to weighted distributions. Sufficient conditions are obtained for the monotonicity of the proposed measure. Characterizations for bivariate exponential conditional distribution are presented based on the proposed measure.     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.     

Smoothing the difference-based estimates of variance using variational mode decomposition

We propose a variational mode decomposition approach to estimate the variance function in a nonparametric heteroscedastic fixed design regression model. A data-driven estimator is constructed by applying variational mode decomposition technique to the difference-based initial estimates. The numerical results show that the proposed estimator performs better than the existing variance estimation procedures in the mean square sense.