双排双波浪带散热水箱传热性能研究

对双排双波浪带散热水箱的结构形式、换热和阻力试验结果进行了分析。采用非线性拟合法得到气侧的换热系数αa，并与桑塔纳管翅式散热水箱的气侧换热系数进行了对比，结果表明，此散热水箱具有较高的换热系数。     

Bayesian methods for generalized linear models with covariates missing at random

The authors propose methods for Bayesian inference for generalized linear models with missing covariate data. They specify a parametric distribution for the covariates that is written as a sequence of one‐dimensional conditional distributions. They propose an informative class of joint prior distributions for the regression coefficients and the parameters arising from the covariate distributions. They examine the properties of the proposed prior and resulting posterior distributions. They also present a Bayesian criterion for comparing various models, and a calibration is derived for it. A detailed simulation is conducted and two real data sets are examined to demonstrate the methodology.     

An approach to evaluating sensitivity in Bayesian regression analyses

This paper presents a method for assessing the sensitivity of predictions in Bayesian regression analyses. In parametric Bayesian analyses there is a family s₀ of regression functions, parametrized by a finite-dimensional vector B. The family s₀ is a subset of R, the set of all possible regression functions. A prior π₀ on B induces a prior on R. This paper assesses sensitivity by computing bounds on the predictive probability of a fixed set K over a class of priors, Γ, induced by a class of families of regression functions, Γ_s, and a class of priors, Γ_π. This paper is divided into three parts which (1) define Γ, (2) describe an algorithm for finding accurate bounds on predictive probabilities over Γ and (3) illustrate the method with two examples. It is found that sensitivity to the family of regression functions can be much more important than sensitivity to π₀.     

An iterative Monte Carlo method for nonconjugate Bayesian analysis

The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). However, the predominance of experience to date resides in applications assuming conjugacy where implementation is reasonably straightforward. This paper describes a tailored approximate rejection method approach for implementation of the Gibbs sampler when nonconjugate structure is present. Several challenging applications are presented for illustration.     

Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines

Abstract.  We consider marginal semiparametric partially linear models for longitudinal/clustered data and propose an estimation procedure based on a spline approximation of the non-parametric part of the model and an extension of the parametric marginal generalized estimating equations (GEE). Our estimates of both parametric part and non-parametric part of the model have properties parallel to those of parametric GEE, that is, the estimates are efficient if the covariance structure is correctly specified and they are still consistent and asymptotically normal even if the covariance structure is misspecified. By showing that our estimate achieves the semiparametric information bound, we actually establish the efficiency of estimating the parametric part of the model in a stronger sense than what is typically considered for GEE. The semiparametric efficiency of our estimate is obtained by assuming only conditional moment restrictions instead of the strict multivariate Gaussian error assumption.     

Two‐Wave Panel Analysis: Comparing Statistical Methods for Studying the Effects of Transitions

Study of the effect of transitions on individual and family outcomes is central to understanding families over the life course. There is little consensus, however, on the appropriate statistical methods needed to study transitions in panel data. This article compares lagged dependent variable (LDV) and change score (CS) methods for analyzing the effect of events in two‐wave panel data. The methods are described, and their performances are compared both with a simulation and a substantive example using the National Survey of Families and Households two‐wave panel. The results suggest that CS methods have advantages over LDV techniques in estimating the effect of events on outcomes in two‐wave panel data.     

Nonparametric M-regression with free knot splines

Many problems of practical interest can be formulated as the nonparametric estimation of a certain function such as a regression function, logistic or other generalized regression function, density function, conditional density function, hazard function, or conditional hazard function. Extended linear modeling provides a convenient theoretical framework for using polynomial splines and their selected tensor products in such function estimation problems and especially for obtaining rates of convergence of the resulting estimates in a unified manner. For a long time the theoretical results were restricted to fixed knot splines and to log-likelihood functions that were twice continuously differentiable. Recently, Stone and Huang extended the theory to handle free knot splines. In the present paper, the theory is further extended to handle contexts in which the log-likelihood function may not be differentiable. Specifically, we establish rates of convergence for estimation based on free knot splines in the context of nonparametric regression corresponding to M-estimates, which includes least absolute deviations (LAD) regression, quantile regression, and robust regression as special cases.     

A copula‐graphic estimator for the conditional survival function under dependent censoring

Rivest Wells (2001) showed that in situations where the dependence between a lifetime and a censoring variable can be modeled by a given Archimedean copula, the copula‐graphic estimator of Zheng Klein (1995) has an explicit form. The authors extend this work to the fixed design regression case. They show that the copula‐graphic estimator then has an asymptotic representation and a Gaussian limit. They also assess the influence of a misspecified copula function on the performance of the estimator. Their developments are illustrated with data on the survival of the Atlantic halibut.     

Schmidt-logistical回归及在股票投资风格分析中的应用

将Gram-Schmidt过程运用到logistical回归模型当中,提出Schmidt-logistical回归方法,该方法在自变量集合中选择对模型解释性最强的信息,删除对因变量无显著解释作用的信息以及重复解释的信息,并把挑选出来的解释变量集合变换成若干直交变量。这样,一方面实现了logistical回归中的变量筛选;同时,Gram-Schmidt 正交化过程的信息分解结构清晰,使得回归模型的解释更加容易。将该方法应用于股票投资风格分析中,有效地进行了变量筛选,实现了股票成长/价值属性的快速判断。