基于Holling Ⅲ型功能反应函数的时滞培养器模型的大范围周期解分析

本文考虑具有简化的HollingⅢ型功能反应函数的时滞单种群微生物连续培养器模型,分析如下系统的大范围周期解问题{s（t）=1-s（t）＋p（s（t））x（t） x（t）=-x（t）＋p（s（t-τ））x（t）,这里p（s）=ms^2/（a＋s^2）.     

ADF单位根检验中联合检验LM统计量研究

 本文研究了ADF单位根检验中参数联合约束的拉格朗日乘数检验。首先,本文构建了4个LM统计量并推导了它们的极限分布;然后,运用蒙特卡罗试验,模拟了有限样本容量常用检验水平下的临界值,拟合了临界值关于样本容量的响应面函数,并总结了LM统计量有限样本容量下的统计特性;比较分析了这4个LM统计量的检验功效及实际检验水平;最后,一个实例分析简要说明了这几个统计量在单位根检验中的应用。     

Overreporting of voting participation as a function of identity

This paper proposes an explanation of the overreporting of voting participation based in Stryker's identity theory (1980), integrating the social pressure approach of Bernstein, Chadha, and Montjoy (2001). A set of logistic regression models is estimated to predict the propensity to overreport using indicators of extensive and intensive commitments to a political identity. Models are tested using vote verification data from five years of the American National Election Studies (1978, 1980, 1984, 1988, and 1990). Identity commitments are strongly predictive of overreporting and conventional understandings of social desirability fail to predict overreporting. Findings suggest that a conceptualization of self-reported voting as a measure of identity may provide a better explanation for why respondents overreport their voting in terms of extensive and intensive commitments to a political identity.     

Extension of Central Composite Design for Second-Order Response Surface Model Building

The central composite design (CCD) is perhaps the most popular class of second-order response surface designs. Even though the CCDs are popular for response surface designs, this class of design has some limitations such as it does not sometimes possess good statistical properties, and it does not fit complicated models well. In this article, we propose extended central composite designs (ECCDs) to overcome these limitations. We compare ECCDs with CCDs in terms of average prediction variance, and find that ECCDs are better than CCDs.     

Robust Estimation of Multi-response Surfaces Considering Correlation Structure

Response surfaces express the behavior of responses and can be used for both single and multi-response problems. A common approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Although there are many methods available in the literature for multiple response optimizations, there are a few studies in model building especially robust models. Assuming correlated responses, in this paper, a robust coefficient estimation method is proposed for multi response problem based on M-estimators. In order to illustrate the performance of the proposed procedure, a contaminated experimental design using a numerical example available in the literature with some modifications is used. Both the classical multivariate least squares method and the proposed robust multivariate approach are used to estimate regression coefficients of multi-response surfaces based on this example. Moreover, a comparison of the proposed robust multi response surface (RMRS) approach with separate robust estimation of single response show that the proposed approach is more efficient.     

An Algorithmic Construction of Four-Level Response Surface Designs

Response surface methodology is widely used for developing, improving, and optimizing processes in various fields. In this article, we present a method for constructing four-level design matrices in order to explore and optimize response surfaces where the predictor variables are each at four equally spaced levels, by utilizing a genetic algorithm. The produced designs achieve both properties of near-rotatability and estimation efficiency.     

Evaluation of Response Surface Designs in Presence of Errors in Factor Levels

Experimenters are often confronted with the problem that errors in setting factor levels cannot be measured. In the robust design scenario, the goal is to determine the design that minimizes the variability transmitted to the response from the variables’ errors. The prediction variance performance of response surface designs with errors is investigated using design efficiency and the maximum and minimum scaled prediction variance. The evaluation and comparison of response surface designs with and without errors in variables are developed for second order designs on spherical regions. The prediction variance and design efficiency results and recommendations for their use are provided.     

Measuring Gross Flows in the Labor Force: An Overview of a Special Conference

The measurement of flows in the American labor market has challenged researchers for years. Several problems, including response variability, rotation group bias, and matching errors, hinder accurate measurement of the flows. The Bureau of the Census and the Bureau of Labor Statistics jointly sponsored a conference to examine these problems, present current research on solutions to the problems, and submit recommendations for improving the data. Recommendations include procedural changes in the Current Population Survey as well as new estimation techniques.     

Reanalyzing Ultimatum Bargaining—Comparing Nondecreasing Curves Without Shape Constraints

We employ a hierarchical Bayesian method with exchangeable prior distributions to estimate and compare similar nondecreasing response curves. A Dirichlet process distribution is assigned to each of the response curves as a first stage prior. A second stage prior is then used to model the hyperparameters. We define parameters which will be used to compare the response curves. A Markov chain Monte Carlo method is applied to compute the resulting Bayesian estimates. To illustrate the methodology, we re-examine data from an experiment designed to test whether experimenter observation influences the ultimatum game. A major restriction of the original analysis was the shape constraint that the present technique allows us to greatly relax. We also consider independent priors and use Bayes factors to compare various models.     

通信反馈法对大学生二语写作效果的实验研究

研究采用定量分析法考查了非英语专业一年级152名大学生的作文测试成绩、性格因素对通信反馈机制功效的影响和学生对通信反馈教学的态度。结果发现基于内容的通信反馈教学比传统批改反馈方式对学生英语写作能力的提高有更积极的促进作用,尤其是在丰富写作内容、增强连贯方面效果较显著。研究能启发我们更好地理解写作的过程、认识和把握写作的本质、帮助提高二语写作教学效果。