Maximum likelihood estimation of bivariate logistic models for incomplete responses with indicators of ignorable and non-ignorable missingness

Summary. Missing observations are a common problem that complicate the analysis of clustered data. In the Connecticut child surveys of childhood psychopathology, it was possible to identify reasons why outcomes were not observed. Of note, some of these causes of missingness may be assumed to be ignorable , whereas others may be non-ignorable . We consider logistic regression models for incomplete bivariate binary outcomes and propose mixture models that permit estimation assuming that there are two distinct types of missingness mechanisms: one that is ignorable; the other non-ignorable. A feature of the mixture modelling approach is that additional analyses to assess the sensitivity to assumptions about the missingness are relatively straightforward to incorporate. The methods were developed for analysing data from the Connecticut child surveys, where there are missing informant reports of child psychopathology and different reasons for missingness can be distinguished.     

用多元智能理论定位英语教师的教学理念

目前学校英语教学的一大弊端就是英语教师的教学视野狭窄,唯课本、"教参"是举,课堂氛围沉闷,学生学习主动性难以发挥;教师还沦为教学的工具,不能充分展现个人的学养、个性和人格特点。这种传统教学上的教师角色已经不能满足现代英语教学的需要。试图通过分析多元智能理论内涵特征及其对英语教学的指导意义来重新定位英语教师的教育理念。     

论科技资源共享管理机关的设置与职权配置

为了有效执行有关科技资源共享的法律、法规及政策,必须建立一个高效的管理机关。然而,科技资源共享涉及众多部门,牵涉利益巨大。科技资源共享管理机关必须与其他科技管理机关的职责相协调。在目前的情况下,设立科技资源共享管理机构需要首先考虑科技资源多头管理模式的现实及其改革路径。具体的方法应当是建立高层次、多部门参加的科技资源共享决策协调机构,并相对集中执行权。同时,在职权设定上应当尽量配置规划、指导、资助等职权。     

A Parametric Bootstrap Approach for One-Way ANOVA Under Unequal Variances with Unbalanced Data

This research is to provide a solution of one-way ANOVA without using transformation when variances are heteroscedastic and group sizes are unequal. Parametric bootstrap test (Krishnamoorthy et al., 2007 Krishnamoorthy, K., Lu, F., Mathew, T. (2007). A parametric bootstrap approach for anova with unequal variances: Fixed and random models. Computational Statistics and Data Analysis 51:5731–5742.[Crossref], [Web of Science ®] , [Google Scholar]) has been shown to be competitive with many other methods when testing the equality of group means. We extend the parametric bootstrap algorithm to a multiple comparison procedure. Simulation results show that the parametric bootstrap approach works well for one-way ANOVA.     

Efficient Stratified Testing Procedure for a False Discovery Rate

The false discovery rate (FDR) has become a popular error measure in the large-scale simultaneous testing. When data are collected from heterogenous sources and form grouped hypotheses testing, it may be beneficial to use the distinct feature of groups to conduct the multiple hypotheses testing. We propose a stratified testing procedure that uses different FDR levels according to the stratification features based on p-values. Our proposed method is easy to implement in practice. Simulations studies show that the proposed method produces more efficient testing results. The stratified testing procedure minimizes the overall false negative rate (FNR) level, while controlling the overall FDR. An example from a type II diabetes mice study further illustrates the practical advantages of this new approach.     

A multiple criteria decision-making model for justifying the acceptance of rush orders

Rush orders are immediate customer demands that exceed the expectation of a currently effective MPS (master production schedule). Decision-makers are often hesitant in the decision of accepting such orders. This paper presents a multiple criteria decision-making model for justifying the acceptance of rush orders for an assembly-to-order production system. Four criteria or production objectives are simultaneously considered and a multiple objective programming technique, the e-constraints approach, is adopted to solve the decision-making problem. This model could give the cost estimation for producing a rush order under various combinations of production objectives. The computed cost value could serve as a valuable reference for justifying the economics of accepting the rush order, and help to determine its pricing strategy.     

A Latent-Variable Model of Quality Determination

Because of data difficulties, there has been little empirical work analyzing the determination of the quality of a firm's output. This article constructs a latent variable model for this problem that uses easily obtainable data. The model is developed from the relationship between the firm's input demand functions and reduced-form output functions, and it has a novel multiple indicator multiple cause (MIMIC) interpretation. The model also allows identification of an intercept, implying that an index of quality that is comparable across samples can be constructed. As an example, a latent variable model of nursing-home quality is estimated.     

Multiple Comparisons with Control Under Stochastic Ordering: Controlling FDR

Multiple comparisons of the effects of several treatments with a control (MCC) has been a central problem in medicine and other areas. Nearly all of existing papers are devoted to comparing means of the effects. To study medical problems more deeply, one needs more information than mean relationship from the given data. It can be expected to get more useful and deeper conclusion by comparing the probability distributions, i.e., by comparison under stochastic orders. This paper presents a likelihood ratio testing procedure to compare effects under stochastic order for MCC problems, controlling the false discovery rate (FDR). Setting a test controlling FDR under stochastic order faces several non trivial problems. These problems are analyzed and solved in this paper. To facilitate the test more easily, the asymptotic p values for the test are used and their distributions are derived. It is shown that controllability of FDR for this comparison procedure can be guaranteed. A real data example is used to illustrate how to apply this testing procedure and what the test can tell. Simulation results show that this testing procedure works quite well, better than some other tests.     

Network effects on organizational decision-making: Blended social mechanisms and IPO withdrawal

This paper develops a new approach to the study of network effects in organizations and markets by proposing that structural influences on social and economic action result from contingent blends of well-understood social mechanisms. We emphasize the interplay of three different network processes: resource and information transfer, status signaling and certification, and social influence. Different mixes of these mechanisms characterize disparate networks because the obligations imposed by ties and the capacities of partners result in situations where mechanisms amplify or diminish one another. We test hypotheses about mechanism interactions using four years (1997–2000) of data on high-technology IPOs that situate organizational decisions about whether to withdraw an offering in two distinct networks. We find that network mechanisms exert multiple moderating effects on one another and that those effects vary systematically across venture capital syndicate and director interlock networks. These findings help to explain why different networks exert disparate effects, why the effects of some structures change as their larger contexts shift, and why even very successful organizations can sometimes find themselves hamstrung by their connections.     

Exact solutions to the Behrens–Fisher Problem: Asymptotically optimal and finite sample efficient choice among

The problem of testing the equality of two normal means when variances are not known is called the Behrens–Fisher Problem. This problem has three known exact solutions, due, respectively, to Chapman, to Prokof’yev and Shishkin, and to Dudewicz and Ahmed. Each procedure has level alpha and power beta when the means differ by a given amount delta, both set by the experimenter. No single-sample statistical procedures can make this guarantee. The most recent of the three procedures, that of Dudewicz and Ahmed, is asymptotically optimal. We review the procedures, and then compare them with respect to both asymptotic efficiency and also (using simulation) in finite samples. Of these exact procedures, based on finite-sample comparisons the Dudewicz–Ahmed procedure is recommended for practical use.