适应于纵数据的随机效应模型中参数的局部影响诊断

本文根据纵数据既包含个体又包含个体不同状态的特点 ,针对适应于纵数据的随机效应模型提出两种便于合理分析数据的扰动方案 ,并给出扰动对参数估计局部影响的各种计算公式和寻找影响点的方法 .通过对 Cambridge过滤嘴中提取尼古丁含量的实验室间数据进行分析表明我们的分析结果不但包含了以前许多学者用不同的方法对这组数据所进行的所有有关影响点方面的分析结果 。

On-Board Data Acquisition System for Automatic Shift System of Vehicle

Data acquisition, analysis and calibrating system affiliated with the vehicle is developed for the research on the automatic shift system (ASS). Considering the vehicle’s hard environment such as vibration, high and low temperature, electromagnetic disturbance and so on, the most suitable project is selected. PC104 transfers data with ECU by serial communication and a solid state disk is used as a FLASH ROM. Some techniques including frequency division of data is adopted in the software design in order to ensure the sampling frequency. The analysis and debug software is also contrived according to the characteristic of the ASS. The system plays an important role in the development of the ASS because of the good reliability and practicability in the application.

Determinants on Rural-Urban Labor Migration in China:Empirical Analysis on the Effect of Rural Cooperate Medical Insurance based on Cross-Sectional and Longitudinal Data

This paper studies factors influencing rural-urban labor migration in China,particularly the implementation of rural cooperative medical insurance(RCMI) in the year 2003.With the support of data analysis from the year 2000,2004 and 2006,clear linear correlations are found between gender,income,health condition and rural-urban labor flow,whereas the impact of education and employment status are more complicated.More importantly,results from regression show that the establishment of RCMI in countryside of China not only inhibits rural residents from seeking employment outside the village,but also pulls back rural people who have already worked in cities.When regional dimension is concerned,the pure composite effect of RCMI on rural labor flow is less significant in coastal areas with better economic performance and medical service.

A moving average Cholesky factor model in joint mean-covariance modeling for longitudinal data

Modeling the mean and covariance simultaneously is a common strategy to efficiently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data. In this article, using generalized estimation equation techniques, we propose a new kind of regression models for parameterizing covariance structures. Using a novel Cholesky factor, the entries in this decomposition have moving average and log innovation interpretation and are modeled as the regression coefficients in both the mean and the linear functions of covariates. The resulting estimators for eovarianee are shown to be consistent and asymptotically normally distributed. Simulation studies and a real data analysis show that the proposed approach yields highly efficient estimators for the parameters in the mean, and provides parsimonious estimation for the covariance structure.

EMPIRICAL LIKELIHOOD FOR LONGITUDINAL PARTIALLY LINEAR MODEL WITH α-MIXING ERRORS

This paper considers large sample inference for the regression parameter in a partially linear regression model with longitudinal data and a-mixing errors. The authors introduce an estimated empirical likelihood for the regression parameter and show that its limiting distribution is a mixture of central chi-squared distributions. Also, the authors derive an adjusted empirical likelihood method which is shown to have a central chi-square limiting distribution. A simulation study is carried out to assess the performance of the empirical likelihood method.

Checking for normality in linear mixed models

Linear mixed models are popularly used to fit continuous longitudinal data, and the random effects are commonly assumed to have normal distribution. However, this assumption needs to be tested so that further analysis can be proceeded well. In this paper, we consider the Baringhaus-Henze-Epps-Pulley （BHEP） tests, which are based on an empirical characteristic function. Differing from their case, we consider the normality checking for the random effects which are unobservable and the test should be based on their predictors. The test is consistent against global alternatives, and is sensitive to the local alternatives converging to the null at a certain rate arbitrarily close to 1/V~ where n is sample size. ^-hlrthermore, to overcome the problem that the limiting null distribution of the test is not tractable, we suggest a new method： use a conditional Monte Carlo test （CMCT） to approximate the null distribution, and then to simulate p-values. The test is compared with existing methods, the power is examined, and several examples are applied to illustrate the usefulness of our test in the analysis of longitudinal data.

Weighted quantile regression for longitudinal data using empirical likelihood

This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application.

Responses to threat in a freshwater invader： longitudinal data reveal personality, habituation, and robustness to changing water temperatures in the ＂killer shrimp＂ Dikerogammarus villosus （Crustacea： Amphipoda）

Freshwater biodiversity and ecosystem integrity are under threat from biological invasions. The ＂killer shrimp＂ Dikerogammarus villosus is a highly predatory amphipod that has spread readily across Central Europe and recently the UK and its arrival has been associated with the significant loss of resident species. Despite this, studies of its behavioral ecology are sparse, even though its be- havior may contribute to its invasion success. For the first time, we investigated antipredator ＂fleeing＂ behavior in D. villosus and how this changed with water temperature. Three key patterns emerged from our analysis. First, within a particular temperature condition there are moderate but consistent among-individual differences in behavior. These are driven by a combination of mean level among-individual differences and within-individual relative consistency in behavior, and pro- vide the key marker for animal personalities. Second, the fleeing responses were not influenced by temperature and third, regardless of temperature, all individuals appeared to habituate to a repeated nondangerous stimulus, indicating a capacity for individual learning. We suggest that the antipreda- tor behavior of D. villosus contributes to its rapid spread and that consistent among-individual differ- ences in behavior may promote biological invasions across heterogeneous conditions. Robustness to changing water temperatures may also be potentially advantageous, particularly in an era of glo- bal climate change, where average temperatures could be elevated and less predictable.

Time-varying latent model for longitudinal data with informative observation and terminal event times

Longitudinal data often occur in follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. We propose a semiparametric mixed effect model with time-varying latent effects in the analysis of longitudinal data with informative observation times and a dependent terminal event. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is provided.