Bayesian Estimation of the Regression Model with Autocorrelated Errors Under Contamination

In this article, the Bayesian analysis of the regression model with errors terms generated by a first-order autoregressive model is considered. Our aim is to study the effect of two kinds of contamination of this model via the posterior distribution of the regression parameter.     

生物技术专业科教结合实践教学模式研究

实践教学是生物技术专业教学活动的重要组成部分,让学生亲身参与科学研究活动,是培养学生创新能力的有效方法.根据生物技术专业的特点并结合科研项目,对实践教学进行了教改研究,提出了将生物技术专业实践教学与科研相结合的创新教学模式.     

中国工业化进程中农村劳动力转移“推力模型”的构建

文章在借鉴“推-拉”理论的基础上．结合中国的实际情况从推力的角度构建农村劳动力转移的“推力模型”，对中国工业化进程中农村劳动力转移的影响因素进行了解释，并提出相应的对策建议。     

Who Attends Senior Centers?

Abstract

Logistic regression analysis was used to compare users and non-users of senior centers. Variables assessing linkage to the service system were more significant predictors of senior center utilization than predisposing, enabling, or need variables. More specifically, users of senior centers were older and more likely to live in rural areas. They also had more social contacts, better mental health, and fewer ADL problems. Senior center users were also more aware of specific service agencies, more likely to consult formal resources in making service decisions, and more likely to have used other services.     

EGARCH Model with Weighted Liquidity

We analyze a variant of the EGARCH model which captures the variation of the intra-day price. We study the asymptotic behavior of the estimators for the parameters of the model. We also illustrate our theoretical results by empirical studies.     

Diagnostics for elliptical linear mixed models with first-order autoregressive errors

For longitudinal time series data, linear mixed models that contain both random effects across individuals and first-order autoregressive errors within individuals may be appropriate. Some statistical diagnostics based on the models under a proposed elliptical error structure are developed in this work. It is well known that the class of elliptical distributions offers a more flexible framework for modelling since it contains both light- and heavy-tailed distributions. Iterative procedures for the maximum-likelihood estimates of the model parameters are presented. Score tests for the presence of autocorrelation and the homogeneity of autocorrelation coefficients among individuals are constructed. The properties of test statistics are investigated through Monte Carlo simulations. The local influence method for the models is also given. The analysed results of a real data set illustrate the values of the models and diagnostic statistics.     

标准化政策对主食产业化的影响

实现主食产业化发展，实行产品的标准化是关键。以豪特林（Hotelling）线形选址模型和塞洛普（Salop）的圆周模型为基础，根据具体情况进行修改后建立一个三阶段的博弈模型讨论主食企业和政府的行为问题。     

Optimal record-based statistical procedures for the two-parameter exponential distribution

There are a number of situations in which the experimental data observed are record statistics. In this paper, optimal confidence intervals as well as uniformly most powerful (MP) tests for one-sided alternatives are developed. Since a uniformly MP test for a two-sided alternative does not exist, generalized likelihood ratio and uniformly unbiased and invariant tests are derived for the two parameters of the exponential distribution based on record data. For illustrative purposes, a data set on the times between consecutive telephone calls to a company's switchboard is analysed using the proposed procedures. Finally, some open problems in this direction are pointed out.     

Multiple Time Series Modeling and Extended Sample Cross-Correlations

This article provides an expository account of the multivariate autoregressive moving average models and proposes an extended sample cross-correlation approach for practical model identification. An iterative model building procedure for applying these models to real data is discussed and demonstrated by analyzing the 5-series U.S. Hog Data.     

Comparison of causal effect estimators under exposure misclassification

Over the past decades, various principles for causal effect estimation have been proposed, all differing in terms of how they adjust for measured confounders: either via traditional regression adjustment, by adjusting for the expected exposure given those confounders (e.g., the propensity score), or by inversely weighting each subject's data by the likelihood of the observed exposure, given those confounders. When the exposure is measured with error, this raises the question whether these different estimation strategies might be differently affected and whether one of them is to be preferred for that reason. In this article, we investigate this by comparing inverse probability of treatment weighted (IPTW) estimators and doubly robust estimators for the exposure effect in linear marginal structural mean models (MSM) with G-estimators, propensity score (PS) adjusted estimators and ordinary least squares (OLS) estimators for the exposure effect in linear regression models. We find analytically that these estimators are equally affected when exposure misclassification is independent of the confounders, but not otherwise. Simulation studies reveal similar results for time-varying exposures and when the model of interest includes a logistic link.