A goodness of fit test for the multilevel proportional odds model

It is essential to test the goodness of fit of the model before making inferences based on it. Multilevel modeling of ordinal categorical responses is not as developed as for continuous responses. Assessing model adequacy in terms of the goodness of fit with ordinal categorical responses is still being developed and no satisfactory tests are available so far. As a consequence of that, this study concentrates on developing such a goodness of fit test for Multilevel Proportional Odds models and to study the properties of the test.     

Joint models for mixed categorical outcomes: a study of HIV risk perception and disease status in Mozambique

Two types of bivariate models for categorical response variables are introduced to deal with special categories such as ‘unsure’ or ‘unknown’ in combination with other ordinal categories, while taking additional hierarchical data structures into account. The latter is achieved by the use of different covariance structures for a trivariate random effect. The models are applied to data from the INSIDA survey, where interest goes to the effect of covariates on the association between HIV risk perception (quadrinomial with an ‘unknown risk’ category) and HIV infection status (binary). The final model combines continuation-ratio with cumulative link logits for the risk perception, together with partly correlated and partly shared trivariate random effects for the household level. The results indicate that only age has a significant effect on the association between HIV risk perception and infection status. The proposed models may be useful in various fields of application such as social and biomedical sciences, epidemiology and public health.     

Phase-II monitoring and diagnosing of multivariate categorical processes using generalized linear test-based control charts

In this paper, two control charts based on the generalized linear test (GLT) and contingency table are proposed for Phase-II monitoring of multivariate categorical processes. The performances of the proposed methods are compared with the exponentially weighted moving average-generalized likelihood ratio test (EWMA-GLRT) control chart proposed in the literature. The results show the better performance of the proposed control charts under moderate and large shifts. Moreover, a new scheme is proposed to identify the parameter responsible for an out-of-control signal. The performance of the proposed diagnosing procedure is evaluated through some simulation experiments.     

基于随机森林模型的分类数据缺失值插补

缺失数据是影响调查问卷数据质量的重要因素,对调查问卷中的缺失值进行插补可以显著提高调查数据的质量。调查问卷的数据类型多以分类型数据为主,数据挖掘技术中的分类算法是处理属性分类问题的常用方法,随机森林模型是众多分类算法中精度较高的方法之一。将随机森林模型引入调查问卷缺失数据的插补研究中,提出了基于随机森林模型的分类数据缺失值插补方法,并根据不同的缺失模式探讨了相应的插补步骤。通过与其它方法的实证模拟比较,表明随机森林插补法得到的插补值准确度更优、可信度更高。     

Comparing mean ranks for repeated measures data

Rank tests are considered that compare t treatments in repeated measures designs. A statistic is given that contains as special cases several that have been proposed for this problem, including one that corresponds to the randomized block ANOVA statistic applied to the rank transformed data. Another statistic is proposed, having a null distribution holding under more general conditions, that is the rank transform of the Hotelling statistic for repeated measures. A statistic of this type is also given for data that are ordered categorical rather than fully rankedo Unlike the Friedman statistic, the statistics discussed in this article utilize a single ranking of the entire sample. Power calculations for an underlying normal distribution indicate that the rank transformed ANOVA test can be substantially more powerful than the Friedman test.     

Imputation procedures for categorical data: their effects on the goodness-of-fit chi-square statistic

An imputation procedure is a procedure by which each missing value in a data set is replaced (imputed) by an observed value using a predetermined resampling procedure. The distribution of a statistic computed from a data set consisting of observed and imputed values, called a completed data set, is affecwd by the imputation procedure used. In a Monte Carlo experiment, three imputation procedures are compared with respect to the empirical behavior of the goodness-of- fit chi-square statistic computed from a completed data set. The results show that each imputation procedure affects the distribution of the goodness-of-fit chi-square statistic in 3. different manner. However, when the empirical behavior of the goodness-of-fit chi-square statistic is compared u, its appropriate asymptotic distribution, there are no substantial differences between these imputation procedures.     

Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials

In situations where the structure of one of the variables of a contingency table is ordered recent theory involving the augmentation of singular vectors and orthogonal polynomials has shown to be applicable for performing symmetric and non-symmetric correspondence analysis. Such an approach has the advantage of allowing the user to identify the source of variation between the categories in terms of components that reflect linear, quadratic and higher-order trends. The purpose of this paper is to focus on the study of two asymmetrically related variables cross-classified to form a two-way contingency table where only one of the variables has an ordinal structure.     

Bayesian Estimation of Survival Functions under Stochastic Precedence

When estimating the distributions of two random variables, X and Y, investigators often have prior information that Y tends to be bigger than X. To formalize this prior belief, one could potentially assume stochastic ordering between X and Y, which implies Pr(X < or = z) > or = Pr(Y < or = z) for all z in the domain of X and Y. Stochastic ordering is quite restrictive, though, and this article focuses instead on Bayesian estimation of the distribution functions of X and Y under the weaker stochastic precedence constraint, Pr(X < or = Y) > or = 0.5. We consider the case where both X and Y are categorical variables with common support and develop a Gibbs sampling algorithm for posterior computation. The method is then generalized to the case where X and Y are survival times. The proposed approach is illustrated using data on survival after tumor removal for patients with malignant melanoma.     

康德定言命令的道德变革与实践困境

康德以纯粹理性形而上学为基础,强调善良意志和定言命令的实践理性价值.以"定言命令"为核心的道德形而上学实践理性排斥伦理学经验论,弘扬人的主体能动性,在西方伦理学引起了重大的道德哲学方向性转变.然而,"定言命令"具有形式化和僵化的普遍化特征,存在严重的形而上矛盾和现实困境,受到黑格尔等人的伦理学批判以及马克思主义唯物主义实践论的超越.     

从结果、行为到行为人：隐性采访的伦理抉择

道德困惑一直是隐性采访争论的焦点，功利主义、绝对命令、中庸之道等西方道德哲学在一定程度上能解读隐性采访面临的伦理困境。从行为的结果、行为本身到行为人，三种伦理学关注的焦点形成了一个呈逆时针方向的圆圈。功利主义是务实的，绝对命令维护了道德的纯洁性，中庸之道强调行为主体的道德修养，在这三种伦理框架的综合指导运用下，隐性采访完成了从客体到主体、由客观到主观的推理过程，伦理抉择变得更为连贯和一致，渐渐成为一种内涵。