Weibull inference using trimmed samples and prior information

Trimmed samples are commonly used in several branches of statistical methodology, especially when the presence of contaminated data is suspected. Assuming that certain proportions of the smallest and largest observations from a Weibull sample are unknown or have been eliminated, a Bayesian approach to point and interval estimation of the scale parameter, as well as hypothesis testing and prediction, is presented. In many cases, the use of substantial prior information can significantly increase the quality of the inferences and reduce the amount of testing required. Some Bayes estimators and predictors are derived in closed-forms. Highest posterior density estimators and credibility intervals can be computed using iterative methods. Bayes rules for testing one- and two-sided hypotheses are also provided. An illustrative numerical example is included.     

Target estimation for the logistic regression model

The method of target estimation developed by Cabrera and Fernholz [(1999). Target estimation for bias and mean square error reduction. The Annals of Statistics, 27(3), 1080–1104.] to reduce bias and variance is applied to logistic regression models of several parameters. The expectation functions of the maximum likelihood estimators for the coefficients in the logistic regression models of one and two parameters are analyzed and simulations are given to show a reduction in both bias and variability after targeting the maximum likelihood estimators. In addition to bias and variance reduction, it is found that targeting can also correct the skewness of the original statistic. An example based on real data is given to show the advantage of using target estimators for obtaining better confidence intervals of the corresponding parameters. The notion of the target median is also presented with some applications to the logistic models.     

李源澄先生经史成就述论

李源澄是我国近现代著名学者。他早年在四川、江苏、河南等地求学,后在江苏、北京、四川、云南、重庆等省市从事教学、科研活动,与古今、新旧、老辈与同辈学者均有广泛的师承及交游关系,因而,他在经学、史学等领域学术研究上成果颇丰,为后人留下了比较丰富的精神财富,在中国近现代学术史上占有一席之地。     

Estimating Merging Costs by Merger Preconditions

This article provides a method for estimating the bounds of transaction costs in horizontal mergers. Consider, for example, a completed monopoly merger in linear Cournot oligopolies with 10 symmetric firms. The method shows that its transaction costs are at most 25% (78%) of total premerger profits if there is zero (100%) excess capacity. Such estimations can be extended in a straightforward manner to other mergers and other oligopoly models. The estimation is based both on the profitability precondition, and on the non-empty core precondition, which postulates that the split of a merger’s profits be in its core. The article shows that the core in linear Cournot oligopolies has a non-empty interior, and indicates that the non-empty core precondition also sheds new lights on understanding important issues such as the stylized fact that mergers are likely to occur in markets plagued by excess capacities; why profitable mergers might not be formed; and why completed mergers might break up in the future.      

工科新生英语学习观念和策略调查报告

本文作者对广州大学２０００级工科生进行了一次英语学习观念和策略的调查．结果表明：（１）被试的语言学习观念大体趋同，但观念不一定导致相应的策略；（２）被试运用学习策略时在情意策略和自主学习管理方面表现出差异与不足；（３）与学习成绩呈显著相关的是反映学习者兴趣和自学能力的策略．本文还就新生英语学习观念和策略的训练提出建议．     

Parameter estimation in semi-linear models using a maximal invariant likelihood function

In this paper, we consider the problem of estimation of semi-linear regression models. Using invariance arguments, Bhowmik and King [2007. Maximal invariant likelihood based testing of semi-linear models. Statist. Papers 48, 357–383] derived the probability density function of the maximal invariant statistic for the non-linear component of these models. Using this density function as a likelihood function allows us to estimate these models in a two-step process. First the non-linear component parameters are estimated by maximising the maximal invariant likelihood function. Then the non-linear component, with the parameter values replaced by estimates, is treated as a regressor and ordinary least squares is used to estimate the remaining parameters. We report the results of a simulation study conducted to compare the accuracy of this approach with full maximum likelihood and maximum profile-marginal likelihood estimation. We find maximising the maximal invariant likelihood function typically results in less biased and lower variance estimates than those from full maximum likelihood.     

Applications of asymptotic confidence intervals with continuity corrections for asymmetric comparisons in noninferiority trials

A large‐sample problem of illustrating noninferiority of an experimental treatment over a referent treatment for binary outcomes is considered. The methods of illustrating noninferiority involve constructing the lower two‐sided confidence bound for the difference between binomial proportions corresponding to the experimental and referent treatments and comparing it with the negative value of the noninferiority margin. The three considered methods, Anbar, Falk–Koch, and Reduced Falk–Koch, handle the comparison in an asymmetric way, that is, only the referent proportion out of the two, experimental and referent, is directly involved in the expression for the variance of the difference between two sample proportions. Five continuity corrections (including zero) are considered with respect to each approach. The key properties of the corresponding methods are evaluated via simulations. First, the uncorrected two‐sided confidence intervals can, potentially, have smaller coverage probability than the nominal level even for moderately large sample sizes, for example, 150 per group. Next, the 15 testing methods are discussed in terms of their Type I error rate and power. In the settings with a relatively small referent proportion (about 0.4 or smaller), the Anbar approach with Yates’ continuity correction is recommended for balanced designs and the Falk–Koch method with Yates’ correction is recommended for unbalanced designs. For relatively moderate (about 0.6) and large (about 0.8 or greater) referent proportion, the uncorrected Reduced Falk–Koch method is recommended, although in this case, all methods tend to be over‐conservative. These results are expected to be used in the design stage of a noninferiority study when asymmetric comparisons are envisioned. Copyright © 2013 John Wiley & Sons, Ltd.     

An online estimation scheme for a Hull–White model with HMM-driven parameters

This paper considers the implementation of a mean-reverting interest rate model with Markov-modulated parameters. Hidden Markov model filtering techniques in Elliott (1994, Automatica, 30:1399–1408) and Elliott et al. (1995, Hidden Markov Models: Estimation and Control. Springer, New York) are employed to obtain optimal estimates of the model parameters via recursive filters of auxiliary quantities of the observation process. Algorithms are developed and implemented on a financial dataset of 30-day Canadian Treasury bill yields. We also provide standard errors for the model parameter estimates. Our analysis shows that within the dataset and period studied, a model with two regimes is sufficient to describe the interest rate dynamics on the basis of very small prediction errors and the Akaike information criterion.     

上市公司资产重组引发的业绩评价指标的思考

本文主要对上市公司资产重组引发的上市公司业绩评价指标的优缺点进行了分析 ,进而提出了新的评价指标投资资本报酬率、经济增加值 ,以图能够对上市公司业绩进行更准确的评价     

Estimation in singular partitioned,reduced or transformed linear models

A singular partitioned linear model, i.e. the singular model comprising the main parameters and the nuisance parameters, can be reduced, or transformed to the form in which only linear functions concerning main parameters are involved. In the paper some properties of the best linear unbiased estimators of these functions following from these models are considered.