Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

Efficacy of Statistical Outlier Analysis for Monitoring Quality of Care

Researchers have proposed that hospitals with excessive statistically unexplained mortality rates are more likely to have quality-of-care problems. The U.S. Health Care Financing Administration currently uses this statistical “outlier” approach to screen for poor quality in hospitals. Little is known, however, about the validity of this technique, since direct measures of quality are difficult to obtain. We use Monte Carlo methods to evaluate the performance of the outlier technique as parameters of the true mortality process are varied. Results indicate that the screening ability of the technique may be very sensitive to how widespread quality-related mortality is among hospitals but insensitive to other factors generally thought to be important.     

大学生村官留任意愿及影响因素分析

利用第一手的大学生村官问卷调查资料,基于Ordered Logistic模型对陕西省282名大学生村官的工作满意度及留任意愿进行了相关分析和回归分析。结果表明：当前大学生村官的工作满意度与留任意愿普遍较低;留任意愿与大学生最初选择从事村官工作的动机紧密相关;大学生村官的性别、年龄、从事村官年限、婚育状况、学历等人口学特征对工作满意度及留任意愿影响显著;社会各界对大学生村官的重视与认可对工作满意度与留任意愿有显著影响;提高大学生村官的收入水平、实行连续三年考核优秀,直接晋升事业编制的政策以及增加晋升为国家公务员等政治机会能够显著提高大学生村官的工作满意度并增强留任意愿,而其它因素对于提高大学生村官的工作满意度与留任意愿的效果并不明显。     

Lassoing the Determinants of Retirement

This article uses Danish register data to explain the retirement decision of workers in 1990 and 1998. Many variables might be conjectured to influence this decision such as demographic, socioeconomic, financial, and health related variables as well as all the same factors for the spouse in case the individual is married. In total, we have access to 399 individual specific variables that all could potentially impact the retirement decision. We use variants of the least absolute shrinkage and selection operator (Lasso) and the adaptive Lasso applied to logistic regression in order to uncover determinants of the retirement decision. To the best of our knowledge, this is the first application of these estimators in microeconometrics to a problem of this type and scale. Furthermore, we investigate whether the factors influencing the retirement decision are stable over time, gender, and marital status. It is found that this is the case for core variables such as age, income, wealth, and general health. We also point out the most important differences between these groups and explain why these might be present.     

New Shrinkage Parameters for the Liu-type Logistic Estimators

The binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively. Therefore, this article introduces new shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) in order to decrease the variance and overcome the problem of multicollinearity. A Monte Carlo study is designed to show the goodness of the proposed estimators over MLE in the sense of mean squared error (MSE) and mean absolute error (MAE). Moreover, a real data case is given to demonstrate the advantages of the new shrinkage parameters.     

Comparative Study of Generalized Estimating Equations and Logistic Regressions on Different Sample Sizes and Correlation Levels

In this study, it was aimed to determine accuracy of generalized estimating equations versus logistic regressions on different correlation levels and sample sizes. For this aim, two methods were compared with different sample sizes 10, 25, 50 and 100 and correlation levels 0.0, 0.3, 0.5 and 0.8. Result of this study showed that using generalized estimating equations could be preferred versus logistic regression when the sample size is over than 25 and correlation level is higher than 0.3 on data taken from studies with repeated measurements, but logistic regression could be better when the autocorrelations do not exist.     

深圳市未来医疗床位需求的模型预测

通过分析深圳市1979-2010年人口变化的数据,采用ARMA模型与多项式回归拟合模型,预测了深圳市2011—2020年的总人口数;再用多项式拟合方法,预测了全市未来10年人口的年龄结构、医疗床位需求总量及各区医疗床位需求量;最后,选取高血压疾病患者的相关数据,预测了患者在不同类型医疗机构就医的医疗床位需求量．     

农民工就业能力的影响因素分析

农民工就业能力问题是研究农民工就业的核心之一,备受关注。从实际调查的566个农民工数据出发,运用二元Logistic模型对农民工就业能力的影响因素进行实证分析。结果表明:年龄、技能专长、外出务工年限、父亲受教育程度、家庭人均年收入、技能培训显著正向影响农民工就业能力,即年龄越大、技能水平越高、外出务工年限越长、父亲受教育程度越高、家庭年收入越高以及接受技能培训有助于农民工就业能力的提高;而性别、受教育程度、村域经济发展水平、母亲受教育程度、养老负担和政府推荐就业等对农民工就业能力没有影响。     

并购企业支付方式选择与内部控制、资本结构——基于logistic回归模型的实证研究

以2008—2010年间沪市上市公司的922起并购案例为研究样本,通过logistic回归方法,实证分析了内部控制水平、资本结构对并购支付方式选择产生的影响。研究发现：与现金支付相比,资产负债率越低,股权支付的可能性越大;内部控制水平越低,股权支付的可能性越小。内部控制水平与资本结构对资产支付及其他支付方式（以混合支付为主）影响不显著。     

生态功能区域农村土地流转行为影响因素研究

厘清农户土地流转行为的主要影响因素,推进流转意愿与流转行为相匹配,对于生态功能区域提升土地利用效率、保障生态用地供给具有重要意义。基于河北省农户调研数据,将二元Logistic回归模型和ROC曲线相结合,深入分析农户土地流转行为的影响因素,并依据ROC曲线检验模型效果和界定最佳预测阈值。模型结果显示ROC曲线的Youden指数为32.7%,对应的预测阈值为0.41,模型效果较为良好,研究结果表明家庭人口规模、承包土地规模、主要收入来源、土地归属认知、村庄地形、村庄类别、租金是影响农户土地流转行为的显著性影响因素,农户土地流转行为发生的影响因素并不限于经济因素的考量。