Wage and Non-Wage Compensation Among Young Alcoholic and Heavy Drinking Women: A Preliminary Analysis

The association between drinking and selected job characteristics among women aged 24 to 31 is examined. Using the 1989 NLSY, women are classified as alcohol abusers or dependent based on DSM-III-R criteria or as heavy drinkers based on reported frequency of six or more drinks. Heavy drinking is negatively associated with wage and non-wage compensation. These effects diminish when human capital measures are controlled. Current alcoholism and current and past heavy drinking are strongly negatively associated with years of schooling. The association between alcoholism and job compensation and characteristics is not as strong as that seen for heavy drinking. It is not known if this is a consequence of errors in identifying alcoholic women in population-based surveys.     

LF拓扑空间的商空间的同胚

利用满映射f:X→Y诱导的L值Zadeh型函数f:L^X→L^Y和LF拓扑空间（L^X,δ）构造商空间（L^Y,μ）；又由f建立X上等价关系－，得出自然投影P：X→X／－，再得用P诱导的L值Zadeh型函数P：L^X→L^X/-和（L^X,δ）构造商空间（L^X/-,μ^*），证明了两商空间同胚。     

On Optimality of Bayesian Wavelet Estimators

Abstract.  We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density. We show that in terms of the mean squared error, for the properly chosen hyperparameters of the prior, all the three resulting Bayesian wavelet estimators achieve optimal minimax rates within any prescribed Besov space     for p  ≥ 2. For 1 ≤  p  < 2, the Bayes Factor is still optimal for (2 s +2)/(2 s +1) ≤  p  < 2 and always outperforms the posterior mean and the posterior median that can achieve only the best possible rates for linear estimators in this case.     

Orlicz教授和他创造性的工作

本文介绍了著名数学家和教育家Orlicz教授和他创造性的工作,以及他对中国数学研究工作的重大影响     

关于参数化测度（Ⅱ）

引进一类拓扑向量空间，用以描述测度的偏导。使用的工具是所谓的参数化鞅。建立了若干参数化鞅的收敛定理。     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context

The aim of this study was to develop a modified quantitative microbial risk assessment (QMRA) framework that could be applied as a decision support tool to choose between alternative drinking water interventions in the developing context. The impact of different household water treatment (HWT) interventions on the overall incidence of diarrheal disease and disability adjusted life years (DALYs) was estimated, without relying on source water pathogen concentration as the starting point for the analysis. A framework was developed and a software tool constructed and then implemented for an illustrative case study for Nepal based on published scientific data. Coagulation combined with free chlorine disinfection provided the greatest estimated health gains in the short term; however, when long‐term compliance was incorporated into the calculations, the preferred intervention was porous ceramic filtration. The model demonstrates how the QMRA framework can be used to integrate evidence from different studies to inform management decisions, and in particular to prioritize the next best intervention with respect to estimated reduction in diarrheal incidence. This study only considered HWT interventions; it is recognized that a systematic consideration of sanitation, recreation, and drinking water pathways is important for effective management of waterborne transmission of pathogens, and the approach could be expanded to consider the broader water‐related context.     

Romantic relationships,college student alcohol use,and negative consequences of drinking

This study examined whether being in a romantic relationship is associated with undergraduates’ alcohol use and negative consequences of drinking. Alcohol use was operationalized to include amount and frequency of drinking, binge drinking, and drunkenness. Negative consequences included: having a hangover, missing a class, getting behind in school work, doing something that was later regretted, forgetting where the student was or what they did, having unplanned sex, and getting hurt or injured. Data came from an online survey distributed to Midwestern undergraduate students (N = 572), with analyses conducted separately for men and women. Results indicated that being in a committed relationship generally served as a protective factor against drunkenness, but did not reduce frequency of drinking or binge drinking. Whereas romantically committed men were only less likely to report doing something that was later regretted, women in romantic relationships were less likely to experience all negative consequences of drinking considered here.     

Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models

Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are available for reduced-rank multivariate generalized linear models. We develop M-estimation theory for concave criterion functions that are maximized over parameter spaces that are neither convex nor closed. These results are used to derive the consistency and asymptotic distribution of maximum likelihood estimators in reduced-rank multivariate generalized linear models, when the response and predictor vectors have a joint distribution. We illustrate our results in a real data classification problem with binary covariates.