协方差改进估计的优良性：计算机模拟结果

对于相依线性回归方程组成的系统.本文对它的回归系数的协方差改进估计(CIE)及其两步估计(TCIE)与最小二乘估计(LSE)进行了计算机模拟比较.模拟结果揭示了这种改进估计的估计的统计优良性.     

具有延迟休假和Min(N,V)-策略控制的M/G/1排队的瞬态队长分布

把"休假延迟"引进到基于多重休假的Min(N,V)-策略排队系统中,研究了有延迟休假和Min(N,V)-策略控制的M/G/1排队系统队长的瞬态性质,其中N是预设的休假终止的门限值.通过使用全概率分解技术和拉普拉斯变换工具,讨论了系统从任意初始状态出发的队长的瞬态分布,获得了队长瞬态分布的拉普拉斯变换表达式.     

混合地理加权空间自回归模型的Profile GMM估计

为了更好地同时考虑空间自相关性和空间异质性,本文研究一类空间自回归混合地理加权回归模型.基于Profile方法和广义矩(GMM)方法,构造了模型中未知空间自回归参数,常数回归系数和系数函数的两类Profile GMM估计.数值模拟结果表明所提出的估计在有限样本中表现良好.     

MCS在概率论与数理统计教学中的应用研究

本文首先介绍了蒙特卡罗模拟,然后在估计事件概率、随机变量分布间的关系、中心极限定理、参数估计、假设检验、方差分析和回归分析中分别介绍了蒙特卡罗模拟的应用,结论是在每种情况下,蒙特卡罗模拟都给理论结果作出了实验证明,表明了蒙特卡罗模拟在概率论与数理统计中有着广泛的应用前景。     

基于惩罚回归样条的积分近似计算与应用

提出一种利用惩罚回归样条拟合被积函数f(x),从而计算复杂积分∫baf(x)dx的新方法.在仅知f(x)带随机扰动的离散数据点集的情况下,利用基于截断幂形式的样条基函数,通过惩罚样条回归,给出函数的多项式拟合结果,再根据该多项式形式便捷计算出积分.模拟和实际应用结果显示该方法计算简单快捷,并具有较好的准确度.     

基于Min(N,D)-策略的M/G/1排队系统的队长分布及最优策略

考虑基于Min(N,D)-策略的M/G/1排队系统,运用全概率分解技术和拉普拉斯变换工具,获得了队长瞬态分布的拉普拉斯变换的递推表达式和稳态队长分布的递推表达式,进一步通过数值实例,讨论了稳态队长分布对系统参数的敏感性,并阐述了稳态队长分布的表达式在系统容量优化设计中的重要价值.最后,建立了费用模型,对最优Min(N,D)-策略与单一的最优N-策略和单一的最优D-策略进行了比较分析.     

纵向数据下线性模型的模态回归估计

本文考虑纵向数据下线性回归模型的稳健估计问题.通过结合模态回归(modal regression)方法和二次推断函数(quadratic inference functions)技术,提出了一种基于模态回归的估计过程.证明了回归系数的估计是相合的,并给出了其渐近分布.数据模拟结果表明所提出的估计方法具有较好的稳健性和有效性.     

PLM中误差方差未知时的GLR

本文检验部分线性回归模型(PLM)中,误差的方差未知时,函数部分是否是线性函数,在备择假设下,先用局部多项式方法估计出函数部分,再估计参数部分.计算出了零假设下广义似然比(GLR)检验统计量的表达式,给出了它的渐近分布,并对结果进行了模拟.     

基于单纯形分布的比例数据的回归分析

很多应用领域中的实验结果都表达成连续比例型数据,这类数据通常度量成为百分比、比率或比例,并取值于单位区间.为了采用弥散模型中的单纯形分布来模拟此类实验结果,本文首先研究单纯形分布的部分重要性质,在回归分析中参数估计和统计推断需要运用这些性质.模拟研究表明,当所研究情形不满足分布假设时,单纯形回归模型比Beta回归和分对数-正态回归模型更为稳健.通过对体外造血干细胞移植技术的真实数据分析,本文阐释这种方法和它针对异常值的稳健性.在R软件中,单纯形回归可以由程序包"simplexreg"实现,读者可以自行下载这个程序包,地址为http://my.zju.edu.cn/share/2466293(验证码:7919).     

固定效应时异系数广义空间自回归模型的估计与模拟

空间面板数据模型常呈现时异特征,现实经济现象中的空间关联多带有时异特性。基于此,本文构建固定效应时异系数广义空间自回归模型,首先采用拟极大似然(QML)方法估计模型并证明参数估计量的渐近性,其次依据贝叶斯(Bayes)公式推出参数后验分布并设计MCMC抽样,最后基于数值模拟比较两种方法在有限样本下的模拟情况,结合具体实例对比分析两种方法的实际估计效果。结果发现:一方面,两种方法的参数模拟均方误差都表现出随样本个体数目的增大而减小,表明增加观测个体数目能显著降低参数模拟偏差。另一方面,Bayes估计的均方误差都小于QML估计,说明Bayes估计比QML估计更可靠。     

ON MIN＇S ZETA-FUNCTION AND HERMITE ELLIPTIC OPERATOR

In this note, the authors study some fundamental properties on a Min＇s zeta- function and explore its connection with Hermite elliptic operator.     

ON MIN'S ZETA-FUNCTION AND HERMITE ELLIPTIC OPERATOR

In this note,the authors study some fundamental properties on a Min's zeta- function and explore its connection with Hermite elliptic operator.     

Empirical likelihood inference for median regression models for censored survival data

Recent advances in median regression model have made it possible to use this model for analyzing a variety of censored survival data. For inference on the model parameter vector, there are now semiparametric procedures based on normal approximation that are valid without strong conditions on the error distribution. However, the accuracy of such procedures can be quite low when the censoring proportion is high. In this paper, we propose an alternative semiparametric procedure based on the empirical likelihood. We define the empirical likelihood ratio for the parameter vector and show that its limiting distribution is a weighted sum of chi-square distributions. Numerical results from a simulation study suggest that the empirical likelihood method is more accurate than the normal approximation based method of Ying et al. (J. Amer. Statist. Assoc. 90 (1995) 178).     

Chaos in the dynamics generated by sequences of maps, with applications to chaotic advection in flows with aperiodic time dependence

In this paper we develop analytical techniques for proving the existence of chaotic dynamics in systems where the dynamics is generated by infinite sequences of maps. These are generalizations of the Conley-Moser conditions that are used to show that a (single) map has an invariant Cantor set on which it is topologically conjugate to a subshift on the space of symbol sequences. The motivation for developing these methods is to apply them to the study of chaotic advection in fluid flows arising from velocity fields with aperiodic time dependence, and we show how dynamics generated by infinite sequences of maps arises naturally in that setting. Our methods do not require the existence of a homoclinic orbit in order to conclude the existence of chaotic dynamics. This is important for the class of fluid mechanical examples considered since one cannot readily identify a homoclinic orbit from the structure of the equations.¶We study three specific fluid mechanical examples: the Aref blinking vortex flow, Samelson's tidal advection model, and Min's rollup-merge map that models kinematics in the mixing layer. Each of these flows is modelled as a type of "blinking flow", which mathematically has the form of a linked twist map, or an infinite sequence of linked twist maps. We show that the nature of these blinking flows is such that it is possible to have a variety of "patches" of chaos in the flow corresponding to different length and time scales.     

Bootstrapping Likelihood for Model Selection with Small Samples

Abstract

Akaike's information criterion (AIC), derived from asymptotics of the maximum likelihood estimator, is widely used in model selection. However, it has a finite-sample bias that produces overfitting in linear regression. To deal with this problem, Ishiguro, Sakamoto, and Kitagawa proposed a bootstrap-based extension to AIC which they called EIC. This article compares model-selection performance of AIC, EIC, a bootstrap-smoothed likelihood cross-validation (BCV) and its modification (632CV) in small-sample linear regression, logistic regression, and Cox regression. Simulation results show that EIC largely overcomes AIC's overfitting problem and that BCV may be better than EIC. Hence, the three methods based on bootstrapping the likelihood establish themselves as important alternatives to AIC in model selection with small samples.     

Approximating Unknown Mappings: An Experimental Evaluation

Different methodologies have been introduced in recent years with the aim of approximating unknown functions. Basically, these methodologies are general frameworks for representing non-linear mappings from several input variables to several output variables. Research into this problem occurs in applied mathematics (multivariate function approximation), statistics (nonparametric multiple regression) and computer science (neural networks). However, since these methodologies have been proposed in different fields, most of the previous papers treat them in isolation, ignoring contributions in the other areas. In this paper we consider five well known approaches for function approximation. Specifically we target polynomial approximation, general additive models (Gam), local regression (Loess), multivariate additive regression splines (Mars) and artificial neural networks (Ann).Neural networks can be viewed as models of real systems, built by tuning parameters known as weights. In training the net, the problem is to find the weights that optimize its performance (i.e. to minimize the error over the training set). Although the most popular method for Ann training is back propagation, other optimization methods based on metaheuristics have recently been adapted to this problem, outperforming classical approaches. In this paper we propose a short term memory tabu search method, coupled with path relinking and BFGS (a gradient-based local NLP solver) to provide high quality solutions to this problem. The experimentation with 15 functions previously reported shows that a feed-forward neural network with one hidden layer, trained with our procedure, can compete with the best-known approximating methods. The experimental results also show the effectiveness of a new mechanism to avoid overfitting in neural network training.     

PLS classification of functional data

Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functions. A simulation study as well as an application to kneading data compare the PLS model results with those given by other methods.     

广义泊松回归模型的推广及其在医疗保险中应用

计数数据往往存在过离散(over-dispersed)即方差大于均值特征,若利用传统的泊松回归模型拟合数据往往会导致其参数的标准误差被低估,显著性水平被高估的错误结论。负二项回归模型、广义泊松回归模型通常被用来处理过离散特征数据。本文以两类广义泊松回归模型GP-1和GP-2模型为基础,将其推广为更为一般的GP-P形式,其中P为参数。此时,P=1或P=2,GP-P模型就退化为GP-1和GP-2模型。文中最后利用此类推广的GP-P模型处理了一组医疗保险数据,并与泊松回归模型、负二项回归模型拟合结果进行了比较。结果表明,推广后的GP-P模型的拟合效果更优。     

近代非线性回归在电力系统负荷短期预测中的应用

本文就河北省１９８５—１９９０年春节期间每小时用电量的统计数字用非线性回归模型（Ｇｏｍｐｅｒｔｄｚ）处理了６年中同日同一时间的耗电量，建立了回归模型，并预测了１９９１年同一时间的用电情况。实际表明本文的结果是可信的．     

LASSO Isotone for High-Dimensional Additive Isotonic Regression

Additive isotonic regression attempts to determine the relationship between a multidimensional observation variable and a response, under the constraint that the estimate is the additive sum of univariate component effects that are monotonically increasing. In this article, we present a new method for such regression called LASSO Isotone (LISO). LISO adapts ideas from sparse linear modeling to additive isotonic regression. Thus, it is viable in many situations with high-dimensional predictor variables, where selection of significant versus insignificant variables is required. We suggest an algorithm involving a modification of the backfitting algorithm CPAV. We give a numerical convergence result, and finally examine some of its properties through simulations. We also suggest some possible extensions that improve performance, and allow calculation to be carried out when the direction of the monotonicity is unknown. Supplemental materials are available online for this article.