Heavy tailed durations of regional rainfall

Durations of rain events and drought events over a given region provide important information about the water resources of the region. Of particular interest is the shape of upper tails of the probability distributions of such durations. Recent research suggests that the underlying probability distributions of such durations have heavy tails of hyperbolic type, across a wide range of spatial scales from 2 km to 120 km. These findings are based on radar measurements of spatially averaged rain rate (SARR) over a tropical oceanic region. The present work performs a nonparametric inference on the Pareto tail-index of wet and dry durations at each of those spatial scales, based on the same data, and compares it with conclusions based on the classical Hill estimator. The results are compared and discussed. The authors express sincere thanks to the Mathematisches Forschungsinstitut Oberwolfach (MFO) for facilitating their collaboration under a “Research in Pairs” project hosted at MFO during March 5–25, 2006. The research of the second and third authors was supported by the project LC06024.     

残差自回归半参数模型的参数估计

研究了残差自回归半参数模型的参数估计,运用广义最小二乘法估计了参数部分．用随机模拟说明了运用广义最小二乘（GLSE）估计出的参数部分优于运用普通最小二乘法（OKSE）得到的估计．     

Semi-parametric estimation of covariate effects using the positive stable frailty model

Many biological and medical studies have as a response of interest the time to occurrence of some event, such as the occurrence of a particular symptom or disease, remission, relapse, death due to some specific disease, or simply death. In this paper we study the problem of assessing the effect of potential risk factors on the outcome event of interest through a parametric or semi-parametric frailty model where the lifetimes have a reason to be considered dependent. This dependence may arise because of multiple endpoints within the same individual or because, when studying a single endpoint, there are natural groupings between study subjects. The objective of this paper is to extend both parametric and semi-parametric approaches to regression analysis in which the lifetimes of individuals in a group are effected by the same random frailty which follows a positive stable distribution. Some comparisons of the properties of this frailty distribution with other frailty distributions are made and an example which assesses the effect of a treatment in a litter-matched tumorigenesis study is presented.     

Optimal estimation in random coefficient regression models

In linear regression models with random coefficients, the score function usually involves unknown nuisance parameters in the form of weights. Conditioning with respect to the sufficient statistics for the nuisance parameter, when the parameter of interest is held fixed, eliminates the nuisance parameters and is expected to give reasonably good estimating functions. The present paper adopts this approach to the problem of estimation of average slope in random coefficient regression models. Four sampling situations are discussed. Some asymptotic results are also obtained for a model where neither the regressors nor the random regression coefficients replicate. Simulation studies for normal as well as non-normal models show that the performance of the suggested estimating functions is quite satisfactory.     

Fitting a Normal Distribution When the Model is Wrong

The paper discusses a likelihood based method of estimation which allows for a small amount of misspecification in the assumption of normality. Asymptotic results suggest that the new method can give an estimated model which is closer to the true model. An application to hearing threshold data is discussed.     

Alternatives to a Semi-Parametric Estimator of Parameters of Rare Events—The Jackknife Methodology*

In this paper, and in a context of regularly varying tails, we propose different alternatives to a well-known estimator of the tail index—the Hill estimator (Hill, 1975). These alternatives have essentially in mind a reduction in bias, preferably without increasing Mean Square Error, by the use of suitable Generalized Jackknife methodologies (Gray and Schucany, 1972). The first estimate obtained through this methodolgy is the one introduced by Peng (1998), under a different context. Other Generalized Jackknife estimators are linear combinations of Hill estimators at different levels. This methodology of affine combinations of Hill estimators at different levels may be easily generalized to other semi-parametric estimators of the tail index, like Pickands' estimator (Pickands, 1975) or the Moment's estimator (Dekkers et al., 1989), and consequently to a general real tail index, seeming to be a promising field of research.     

The Bootstrap Methodology in Statistics of Extremes—Choice of the Optimal Sample Fraction

The main objective of statistics of extremes is the prediction of rare events, and its primary problem has been the estimation of the tail index , usually performed on the basis of the largest k order statistics in the sample or on the excesses over a high level u. The question that has been often addressed in practical applications of extreme value theory is the choice of either k or u, and an adaptive estimation of . We shall be here mainly interested in the use of the bootstrap methodology to estimate adaptively, and although the methods provided may be applied, with adequate modifications, to the general domain of attraction of G, , we shall here illustrate the methods for heavy right tails, i.e. for > 0. Special relevance will be given to the use of an auxiliary statistic that is merely the difference of two estimators with the same functional form as the estimator under study, computed at two different levels. We shall also compare, through Monte Carlo simulation, these bootstrap methodologies with other data-driven choices of the optimal sample fraction available in the literature.     

纵向数据半参数Beta回归模型的影响分析

本将随机效应当作是缺失数据,基于Q函数和EM算法并利用P-样条拟合非参数部分,得到了纵向数据半参数Beta回归模型估计方法.基于数据删除模型,我们得到了模型参数部分的广义Cook距离以及非参数部分的广义DFIT.此外,本文还研究了在四种不同扰动情形下模型的局部影响分析,得到了相应的影响矩阵.最后,我们通过两个数值实例验证了所得诊断统计量的有效性.     

基于半参数单指标方法的公司治理研究

本文以1995-2000年间我国所有上市公司CEO更换的数据为样本,应用半参数单指标方法(single-index semi-parametric methods)分析影响上市公司CEO强制更换的因素,对公司治理的效率进行了初步的判断。研究表明:(1)强制性CEO更换与公司业绩、银行短期贷款比率负相关;(2)当最大股东为国有商业机构时,CEO被强制更换的几率比较大;(3)最大股东的持股比率未能对强制性CEO更换起到显著解释作用。总体而言,公司的治理起到一定的作用。但是银行作为债权人,应加强对上市公司的监管。     

一种基于半参数回归的加速度计误差模型辨识方法

为了解决加速度计离心机试验中系统误差和未建模误差对加速度计模型辨识的影响,将统计学中的半参数回归方法引入到加速度计的模型辨识中,建立了加速度计的半参数回归模型,提出了一种基于最小二乘-半参数回归模型(LS-SPRM)的估计方法,该估计方法利用最小二乘法估计加速度计的误差模型系数,利用半参数回归方法估计加速度计测试中的系统误差,并通过检验残差是否为白噪声作为判断是否有系统误差的条件。在半参数模型的估计中,采用二阶段估计方法,利用三次样条函数进行非参数部分的估计,并讨论了光滑参数的选取方法。仿真试验结果表明,采用该方法能够较好地补偿由于系统误差和未建模误差带来的影响,使加速度计模型辨识的标准差较普通最小二乘法减小45%左右,估计的残差也减小了近一倍。