On structural models and a theorem of d. basu

Structural inference as a method of statistical analysis seems to have escaped the attention of many statisticians. This paper focuses on Fraser’s necessary analysis of structural models as a tool to derive classical distribution results.

A structural model analyzed by Zacks (1971) by means of conventional statistical methods and fiducial theory is re-examined by the structural method. It is shown that results obtained by the former methods come as easy consequences of the latter analysis of the structural model. In the process we also simplify Zacks1 methods of obtaining a minimum risk equivariant estimator of a parameter of the model.

A theorem of Basu (1955), often used to prove independence of a complete sufficient statistic and an ancillary statistic, is also reexamined in the light of structural method. It is found that for structural models more can be achieved by necessary analysis without the use of Basu’s theorem. Bain’s (1972) application of Basu’s theorem of constructing confidence intervals for Weibull reliability is given as an example.     

A new dimension to nonparametric tests

It is demonstrated that a statistician can take a preliminary peek- at the data in a special vay in order to select an appropriate nonparametric test statistic without spoiling the distribution-free property. This process (adaptive selection and then testing) generally improves zhe paver over a vide range of underlying distributions and is the nev -dimension to nonparametric tests. Several illustrations are given using standard nonparametric techniques involving the usual one-sample, tvo-sample, k-sample, and independence testing situations. More recent (and not standard) applications to regression and classification problems are then considered. Finally one 'difficulty concerning this adapting process is noted, but a corrective action is suggested.     

Average Collapsibility of Distribution Dependence and Quantile Regression Coefficients

Abstract. The Yule–Simpson paradox notes that an association between random variables can be reversed when averaged over a background variable. Cox and Wermuth introduced the concept of distribution dependence between two random variables X and Y, and gave two dependence conditions, each of which guarantees that reversal of qualitatively similar conditional dependences cannot occur after marginalizing over the background variable. Ma, Xie and Geng studied the uniform collapsibility of distribution dependence over a background variable W, under stronger homogeneity condition. Collapsibility ensures that associations are the same for conditional and marginal models. In this article, we use the notion of average collapsibility, which requires only the conditional effects average over the background variable to the corresponding marginal effect and investigate its conditions for distribution dependence and for quantile regression coefficients.     

A Simulation Study of the Performance of Five Discriminant Analysis Methods for Mixtures of Continuous and Binary Variables

The present study investigates the performance of fice discrimination methods for data consisting of a mixture of continuous and binary variables. The methods are Fisher’s linear discrimination, logistic discrimination, quadratic discrimination, a kernal model and an independence model. Six-dimensional data, consisting of three binary and three continuous variables, are simulated according to a location model. The results show an almost identical performance for Fisher’s linear discrimination and logistic discrimination. Only in situations with independently distributed variables the independence model does have a reasonable discriminatory ability for the dimensionality considered. If the log likelihood ratio is non-linear ratio is non-linear with respect to its continuous and binary part, the quadratic discrimination method is substantial better than linear and logistic discrimination, followed by the kernel method. A very good performance is obtained when in every situation the better one of linear and quardratic discrimination is used.     

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug‐drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.     

Weighted Estimator for the Linear Transformation Models with Multivariate Failure Time Data

In this article, a simple and efficient weighted method is proposed to improve the estimation efficiency for the linear transformation models with multivariate failure time data. Asymptotic properties of the estimators with a closed-form variance-covariance matrix are established. In addition, a goodness-of-fit test is developed to evaluate the adequacy of the model. The performance of proposed method and the comparison on the efficiency between the proposed method and the working independence method (Lu, 2005) are conducted in finite-sample situation by simulation studies. Finally a real data set from the Busselton Population Health Surveys is illustrated to validate the proposed methodology. The related proofs of the theorems are given in the Appendix.     

A biomedical application of latent class models with random effects

Traditional latent class modelling has been used in many biomedical settings. Unfortunately, many of these applications assume that the diagnostic tests are independent given the true disease status, an assumption that is often violated in practice. Qu, Tan and Kutner developed general latent class models with random effects to model the conditional dependence among multiple diagnostic tests. In this paper latent class modelling with random effects is used to estimate the sensitivity and specificity of six screening tests for detecting Chlamydia trachomatis in endocervical specimens from women attending family planning clinics.     

“东亚福利体制”的外在独立性研究

20世纪90年代以来,东亚福利体制成为学术界讨论的重点之一。有学者赞同东亚福利体制的存在,但是也有学者对东亚福利体制这一概念进行了质疑。由此,对东亚福利体制是否具有外在独立性进行研究就具有相当大的意义。本文在将东亚福利体制与自由主义、保守主义、社会民主主义福利体制的典型国家进行对比后认为,东亚福利体制在福利进程、福利取向、福利文化、福利期待四个方面均具有外在独立性,是与传统三种福利体制并存的独立福利体制。     

论改革我国的审判委员会制度

审判委员会制度是我国特殊历史条件下的产物，在一定时期曾发挥过积极作用。但是随着我国社会主义法治化建设进程的加快和司法改革的深化,审判委员会制度本身所存在的问题也日益突显。针对该情况，探讨了审判委员会制度的历史、性质及现状，并就该制度存在的主要弊端,提出相应的改革建议。     

仪式化立意与表达——中国主流影像文本与负价值的缝合

主流影像文本与正面价值如同一对孪生姐妹,似乎彼此融为一体,不可分割,为此往往形成一种错觉,强调主流影像文本就必须把正面价值放于中心地位,以正面价值的表述作为衡量主流影像文本的重要尺度和砝码.但是,影像文本对价值观念的表述却依照自己的文本逻辑进行,并没有把正价值当成唯一的"标的".把负价值植入叙事活动,通过叙事缝合,形成故事价值的"负向正"的转换,却是大多数中国主流影像文本所采用的仪式化立意与表达策略.