Fitting logistic multilevel models with crossed random effects via Bayesian Integrated Nested Laplace Approximations: a simulation study

Fitting cross-classified multilevel models with binary response is challenging. In this setting a promising method is Bayesian inference through Integrated Nested Laplace Approximations (INLA), which performs well in several latent variable models. We devise a systematic simulation study to assess the performance of INLA with cross-classified binary data under different scenarios defined by the magnitude of the variances of the random effects, the number of observations, the number of clusters, and the degree of cross-classification. In the simulations INLA is systematically compared with the popular method of Maximum Likelihood via Laplace Approximation. By an application to the classical salamander mating data, we compare INLA with the best performing methods. Given the computational speed and the generally good performance, INLA turns out to be a valuable method for fitting logistic cross-classified models.     

Principal weighted logistic regression for sufficient dimension reduction in binary classification

Sufficient dimension reduction (SDR) is a popular supervised machine learning technique that reduces the predictor dimension and facilitates subsequent data analysis in practice. In this article, we propose principal weighted logistic regression (PWLR), an efficient SDR method in binary classification where inverse-regression-based SDR methods often suffer. We first develop linear PWLR for linear SDR and study its asymptotic properties. We then extend it to nonlinear SDR and propose the kernel PWLR. Evaluations with both simulated and real data show the promising performance of the PWLR for SDR in binary classification.     

零售型企业的供应商分类管理研究

供应链管理是企业在当前日益激烈的竞争环境中为提高核心竞争力而形成发展起来的管理方法。供应商的选择与管理是供应链管理中的研究热点,尤其是在零售领域。一个零售商往往拥有众多的供应商,对这些供应商进行分类管理是降低运营成本、提高管理水平的重要途径。此外,对供应商进行分类管理还能够促进双方在更深层面上的战略性合作,通过互动交流提高整体运作绩效。本文通过对不同类型供应商的比较分析指出,将供应商按产品优势和服务优势进行二元分类最能体现供应商间的本质性区别,并且也是管理成本相对最低的分类方法,我们以其各自的突出优势点将供应商分为产品型供应商与服务型供应商两类。本文根据零售型企业的供应商特点给出了供应商分类的指标集,并利用判别分析给出了相应的定量化分类方法及应用实例。     

Comparison of Bayesian sample size criteria: ACC, ALC, and WOC

A challenge for implementing performance-based Bayesian sample size determination is selecting which of several methods to use. We compare three Bayesian sample size criteria: the average coverage criterion (ACC) which controls the coverage rate of fixed length credible intervals over the predictive distribution of the data, the average length criterion (ALC) which controls the length of credible intervals with a fixed coverage rate, and the worst outcome criterion (WOC) which ensures the desired coverage rate and interval length over all (or a subset of) possible datasets. For most models, the WOC produces the largest sample size among the three criteria, and sample sizes obtained by the ACC and the ALC are not the same. For Bayesian sample size determination for normal means and differences between normal means, we investigate, for the first time, the direction and magnitude of differences between the ACC and ALC sample sizes. For fixed hyperparameter values, we show that the difference of the ACC and ALC sample size depends on the nominal coverage, and not on the nominal interval length. There exists a threshold value of the nominal coverage level such that below the threshold the ALC sample size is larger than the ACC sample size, and above the threshold the ACC sample size is larger. Furthermore, the ACC sample size is more sensitive to changes in the nominal coverage. We also show that for fixed hyperparameter values, there exists an asymptotic constant ratio between the WOC sample size and the ALC (ACC) sample size. Simulation studies are conducted to show that similar relationships among the ACC, ALC, and WOC may hold for estimating binomial proportions. We provide a heuristic argument that the results can be generalized to a larger class of models.     

Causal relationships between background characteristics, service utilization, satisfaction, and service outcomes (school performance): A path analysis

This study develops a causal model to examine the relationships between service outcome (school performance) and consumer satisfaction, service utilization, and demographic characteristics of the youth receiving mental health services. Data for 281 children, aged 12–18, served from April 2003 to December 2004 were used for the analyses. Causal relationships among variables of the proposed model were tested by using AMOS, and the respecified model was accepted as the final model with good fit to the data (2 = 5.099, df = 8, NFI = .951, CFI = .998, RMSEA = .001). Results of the model test point to the importance of the children's age, service intensity, and consumer satisfaction as determinants of school performance outcome. In particular, increased worker satisfaction and overall satisfaction lead to increases in the perceived success satisfaction and this satisfaction, in turn, leads improvement in grades. Efforts to improve service satisfaction are emphasized and studies based on a more dynamic perspective are needed.     

General Outcome Measures for Verbal Operants

A general outcome measure (GOM) can be used to show progress towards a long-term goal. GOMs should sample domains of behavior across ages, be sensitive to change over time, be inexpensive and easy to use, and facilitate decision making. Skinner''s (1957) analysis of verbal behavior may benefit from the development of GOM. To develop GOM, we conducted a review of the literature on mands, tacts, echoics, and intraverbals. The four areas reviewed included (a) an examination of the participant''s response form (i.e., vocal or nonvocal), (b) the type of prompt used, (c) types of materials used, and (d) timing of responses or sessions. Based on the review of the literature, we developed GOM for mands and tacts. This paper attempts to bridge the concept of GOMs with Skinner''s analysis of verbal behavior.     

当代大学生的择业行为分析:自我知觉视角

当前我国大学生就业困难已经成为社会上普遍关注的焦点问题,很多学者从多学科、多视角对大学生就业难的原因进行了一定分析.对大学生个体择业行为的考察应作为大学生就业问题研究的重要基点之一.从自我知觉层面对大学生择业行为进行分析与阐述,认为大学生的自我概念、自我效能感、结果期待与个人目标是导致他们择业行为不当的重要个人因素,并从学生自身、高校和学生家庭等三方面提出了改善大学生择业质量的相应措施.     

基于赔付期望的财产保险业产出模型研究

本文从《中国保险年鉴》和中国保监局广东省监管局网站获取广东省（不含深圳）财产保险业统计数据,应用移动平均值法和适应性预期法对赔付率进行平滑;鉴于赔付率数据能够较好拟合差分自回归移动平均模型,采用小波降噪去除白噪声的方法,平滑赔付率数据。三种方法中,小波降噪平滑后的数据与原数据误差最小,平滑效果最好。三种平滑方法均能够抵消巨额未决赔款拉低总产出的影响,较好地平滑了财产保险业的总产出。     

Regularized robust estimation in binary regression models

In this paper, we investigate robust parameter estimation and variable selection for binary regression models with grouped data. We investigate estimation procedures based on the minimum-distance approach. In particular, we employ minimum Hellinger and minimum symmetric chi-squared distances criteria and propose regularized minimum-distance estimators. These estimators appear to possess a certain degree of automatic robustness against model misspecification and/or for potential outliers. We show that the proposed non-penalized and penalized minimum-distance estimators are efficient under the model and simultaneously have excellent robustness properties. We study their asymptotic properties such as consistency, asymptotic normality and oracle properties. Using Monte Carlo studies, we examine the small-sample and robustness properties of the proposed estimators and compare them with traditional likelihood estimators. We also study two real-data applications to illustrate our methods. The numerical studies indicate the satisfactory finite-sample performance of our procedures.     

Clinicians Play with Mud,and It's the Best Thing for Us: An Interview With Alistair Campbell

Alistair Campbell is Research Editor for the ANZJFT. He is a Senior Lecturer in Clinical Psychology and Director of the Psychology Clinic at James Cook University in Townsville. He speaks provocatively with Ben Hansen about research and training in family therapy, about accountability to our clients, and about practice.