大学"专业英语"课程教学改革探讨

文章对大学“专业英语”课程的教学改革进行探讨 ,提出了一种教与学互动的教学方法。在该方法中 ,每一堂课都有明确的目的及达到该目的所采用的方法。学生在每堂课中都有学习和实践环节 ,从而达到对所学内容掌握和应用的目的。     

ON CONFIDENCE REGIONS OF EMBEDDED MODELS IN REGULAR PARAMETRIC FAMILIES (A GEOMETRIC APPROACH)

Regular parametric families are commonly encountered in statistical problems (e.g. Cox & Hinkley, 1974). In this paper, we propose a differential geometric framework for the embedded models in these families. Our framework may be regarded as an extension of that presented by Bates & Watts (1980) for nonlinear regression models. As an application, we use this geometric framework to derive three kinds of improved approximate confidence regions for the parameter and parameter subsets in terms of curvatures. The results obtained by Hamilton et al. (1982) and Hamilton (1986) are extended to embedded models in regular parametric families.     

A Geometric Approach to Principal Components Analysis

The net promoter score (NPS) is a novel summary statistic used by thousands of companies as a key performance indicator of customer loyalty. While adoption of the statistic has grown rapidly over the last decade, there has been little published on its statistical properties. Common interval estimation techniques are adapted for use with the NPS, and performance assessed on the largest available database of companies’ net promoter scores. Variations on the adjusted Wald, and an iterative score test are found to have superior performance.     

Using generalized doubly robust estimator to estimate average treatment effects of multiple treatments in observational studies

The generalized doubly robust estimator is proposed for estimating the average treatment effect (ATE) of multiple treatments based on the generalized propensity score (GPS). In medical researches where observational studies are conducted, estimations of ATEs are usually biased since the covariate distributions could be unbalanced among treatments. To overcome this problem, Imbens [The role of the propensity score in estimating dose-response functions, Biometrika 87 (2000), pp. 706–710] and Feng et al. [Generalized propensity score for estimating the average treatment effect of multiple treatments, Stat. Med. (2011), in press. Available at: http://onlinelibrary.wiley.com/doi/10.1002/sim.4168/abstract] proposed weighted estimators that are extensions of a ratio estimator based on GPS to estimate ATEs with multiple treatments. However, the ratio estimator always produces a larger empirical sample variance than the doubly robust estimator, which estimates an ATE between two treatments based on the estimated propensity score (PS). We conduct a simulation study to compare the performance of our proposed estimator with Imbens’ and Feng et al.’s estimators, and simulation results show that our proposed estimator outperforms their estimators in terms of bias, empirical sample variance and mean-squared error of the estimated ATEs.     

Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem; however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (KMC), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the KMC algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the KMC algorithm to group similar subjects based on the transformed GPS.     

Optimality of quasi-score in the multivariate mean–variance model with an application to the zero-inflated Poisson model with measurement errors

In a multivariate mean–variance model, the class of linear score (LS) estimators based on an unbiased linear estimating function is introduced. A special member of this class is the (extended) quasi-score (QS) estimator. It is ‘extended’ in the sense that it comprises the parameters describing the distribution of the regressor variables. It is shown that QS is (asymptotically) most efficient within the class of LS estimators. An application is the multivariate measurement error model, where the parameters describing the regressor distribution are nuisance parameters. A special case is the zero-inflated Poisson model with measurement errors, which can be treated within this framework.     

Latent Variable Models in Heterogeneous Spaces for Observations of Mixed Types

The maximum likelihood approach to the estimation of factor analytic model parameters most commonly deals with outcomes that are assumed to be multivariate Gaussian random variables in a homogeneous input space. In many practical settings, however, many studies needing factor analytic modeling involve data that, not only are not multivariate Gaussian variables, but also come from a partitioned input space. This article introduces an extension of the maximum likelihood factor analysis that handles multivariate outcomes made up of attributes with different probability distributions, and originating from a partitioned input space. An EM Algorithm combined with Fisher Scoring is used to estimate the parameters of the derived model.     

A combined SR-CUSUM procedure for detecting common changes in panel data

Abstract

A procedure based on the sum of N Shiryayev–Roberts processes is proposed to detect common changes in panel data and shown to perform better for small portions of changed panels. The change-point for each panel is estimated by using the CUSUM process calculated in parallel. The changed panels are isolated by using the scores formed by the post-change parameter estimations and the common change point is then estimated from the isolated changed panels. A real example is used for illustration. An adaptive detection procedures is also proposed when the unknown post-change parameters are estimated adaptively in each panel.     

Optimality of marginal likelihood estimating equations

In the presence of nuisance parameters, Godambe (1976) showed an optimality property of the conditional efficient score. We derive analogous results for marginal likelihood when the remainder of the likelihood is conditionally complete. The method of deriving this result is shown to be useful for deriving other optimality results. including optimality of the partial score.     

Comparing distributions of time to onset of disease in animal tumorigenicity experiments

The cause-of-death test of Peto et al.(1980)pools information from a Hoel-Walburg test on incidental tumors with information from a logrank test on fatal tumors in order to compare the tumor rate of a group of rodents exposed to a carcinogen against the tumor rate of a group of unexposed animals. The cause-of-death test, which can arise as a partial likelihood score test from a model that assumes proportional odds for tumor prevalence and proportional hazards for tumor mortality, is not, in general, a direct test for equality of tumor onset distributions for occult tumors that are observed in both fatal and incidental contexts. This paper develops a direct cause-of-death test for comparing distributions of time to onset of occultumors. The test is derived as a partial likelihood score test under an assumed proportional hazards model for tumor onset distributions. The size and power of the proposed test are compared in a Monte Carlo simulation study to the size and power of competitive procedures, including procedures that do not require cause-of-death information.