三相异步电动机任意负载下参数计算

提出了异步电机在任意负载下十种参数的计算公式，对分析、研究电机的运行特性提供了理论基础，既经济实用又简便准确，并举例说明。     

On expected volumes of multidimensional confidence sets associated with the usual and adjusted likelihoods

We consider a general multiparameter set-up, where both the interest and the nuisance parameters are possibly vector valued. We derive an explicit higher order asymptotic formula to compare the expected volumes of confidence sets given by likelihood ratio statistics arising from the usual profile likelihood and various adjustments thereof. Our general framework also allows us to include highest posterior density regions, with approximate frequentist validity, in the study. The fact that our interest parameter is possibly vector valued complicates the derivation and warrants the development of special tools and techniques.     

计算机数控编程“变量”参数的应用

在机械加工中应用数控进行有效控制的变量参数是客观存在的。如何在计算机数控编程中对同一类型设定不同的变量是提高加工效率、减少编程程序的一项关键技术。介绍几种应用计算机数控编程类型，供不同类型的变量参数在计算机数控编程中借鉴。     

从“意义参数”的角度评新版《呼啸山庄》

文章试图在文学翻译批评中引入“意义参数”的概念,并结合《呼啸山庄》的最新中译本从语境、情感、形象、风格、语法五个意义参数方面对其做简要评析。目的在于明确文学翻译中单纯的语言上的忠实并不意味着一定能产生最好的译本,非文本因素同样起着不可忽视的作用。     

辽宁省就业和经济增长关系的时变参数模型

在讨论就业和经济增长之间关系的理论基础上,分析辽宁省就业弹性偏低和奥肯定律发生重大变异的机理,然后指出随时间变化的就业弹性系数才是分析辽宁省就业和经济增长之间关系的重要前提,最后构建辽宁省就业与经济增长之间关系的时变参数模型。     

Generalised partial linear single-index mixed models for repeated measures data

In this paper, we propose generalised partial linear single-index mixed models for analysing repeated measures data. A penalised quasi-likelihood approach using P-spline is used to estimate the nonparametric function, linear parameters, and single-index coefficients. Asymptotic properties of the estimators are developed when the dimension of spline basis grows with increasing sample size. Simulation examples and two applications: the study of health effects of air pollution in North Carolina, and treatment effect of naltrexone on health costs for alcohol-dependent individuals, illustrate the effectiveness of our approach.     

Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data

We implement a joint model for mixed multivariate longitudinal measurements, applied to the prediction of time until lung transplant or death in idiopathic pulmonary fibrosis. Specifically, we formulate a unified Bayesian joint model for the mixed longitudinal responses and time-to-event outcomes. For the longitudinal model of continuous and binary responses, we investigate multivariate generalized linear mixed models using shared random effects. Longitudinal and time-to-event data are assumed to be independent conditional on available covariates and shared parameters. A Markov chain Monte Carlo algorithm, implemented in OpenBUGS, is used for parameter estimation. To illustrate practical considerations in choosing a final model, we fit 37 different candidate models using all possible combinations of random effects and employ a deviance information criterion to select a best-fitting model. We demonstrate the prediction of future event probabilities within a fixed time interval for patients utilizing baseline data, post-baseline longitudinal responses, and the time-to-event outcome. The performance of our joint model is also evaluated in simulation studies.     

A Consistent Method of Estimation For The Three-Parameter Gamma Distribution

For the three-parameter gamma distribution, it is known that the method of moments as well as the maximum likelihood method have difficulties such as non-existence in some range of the parameters, convergence problems, and large variability. For this reason, in this article, we propose a method of estimation based on a transformation involving order statistics from the sample. In this method, the estimates always exist uniquely over the entire parameter space, and the estimators also have consistency over the entire parameter space. The bias and mean squared error of the estimators are also examined by means of a Monte Carlo simulation study, and the empirical results show the small-sample superiority in addition to the desirable large sample properties.     

Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters

Regularized variable selection is a powerful tool for identifying the true regression model from a large number of candidates by applying penalties to the objective functions. The penalty functions typically involve a tuning parameter that controls the complexity of the selected model. The ability of the regularized variable selection methods to identify the true model critically depends on the correct choice of the tuning parameter. In this study, we develop a consistent tuning parameter selection method for regularized Cox's proportional hazards model with a diverging number of parameters. The tuning parameter is selected by minimizing the generalized information criterion. We prove that, for any penalty that possesses the oracle property, the proposed tuning parameter selection method identifies the true model with probability approaching one as sample size increases. Its finite sample performance is evaluated by simulations. Its practical use is demonstrated in The Cancer Genome Atlas breast cancer data.