非对称广义自回归条件异方差的新模型

本文提出了一个新的非对称广义自回归条件异方差的新模型,证明了该模型宽平稳及其最简模型偶数价矩存在的充要条件。     

关于广义线形回归参数极大似然估计相合性的若干问题

本文在广义线性回归中响应变量服从指数型分布且有自然联系的情况下, 讨论了模型参数的极大似然估计的相合性条件有关的若干问题.     

广义线性回归极大似然估计的强相合性

设有该文第1节所描述的广义线性回归模型,以$underline{lambda}_n$和$overline{lambda}_n$分别记$sumlimits_{i=1}^{n}Z_iZ_i^{prime}$的最小和最大特征根,$hat{beta}_n$记$beta_0$的极大似然估计.在文献[1]中,当{$Z_i,ige1$}有界时得到$hat{beta}_n$强相合的充分条件,在自然联系和非自然联系下分别为$underline{lambda}_nrightarrowinfty$, $(overline{lambda}_n)^{1/2+delta}=O(underline{lambda}_n)$（对某$delta>0$)以及$underline{lambda}_nrightarrowinfty$, $overline{lambda}_n=O(underline{lambda}_n)$.作者将后一结果改进为只要求$(overline{lambda}_n)^{1/2+delta}=O(underline{lambda}_n)$,从而与自然联系情况下的条件达到一致.     

带协变量的负二项回归模型中离散参数估计的研究

针对带协变量的负二项回归模型中离散参数估计问题,推广了极大似然估计和Bootstrap极大似然估计方法,并在绝对偏差的意义下,通过模拟研究和实际数据分析研究了估计的优良性.研究结果表明协变量和样本量均对离散参数估计有影响.     

保序回归与最大似然估计

约束条件下的统计推断巳成为统计分析中一个重要的研究领域,而保序回归的研究又是其中之关键。本文通过一个实例引导出统计模型,比较系统地总结了保序回归的性质、求解方法,以及与最大似然估计之间的关系。本文还把问题扩展到多维保序回归和广义保序回归。     

基于空间自回归模型的缺失值插补方法

本文研究来自于区域的截面数据中缺失值的插补问题,讨论了当数据中存在空间相关时,空间自回归模型的建立以及利用其对缺失值进行插补的方法,并根据实际数据,通过建立模型给出插补结果。     

线性约束下异方差回归模型参数的极大似然估计

运用参数的极大似然估计法,给出在线性约束条件Hβ=C下异方差回归模型参数β和λ的极大似然估计,并讨论了估计参数的性质和模型的残差.利用得到的结论对线性约束下异方差回归模型的进一步研究和应用具有一定的理论和实际价值.     

非线性回归模型的经验似然诊断

经验似然方法已经被广泛用于线性模型和广义线性模型.本文基于经验似然方法对非线性回归模型进行统计诊断.首先得到模型参数的极大经验似然估计;其次基于经验似然研究了三种不同的影响曲率度量;最后通过一个实际例子,说明了诊断方法的有效性.     

Logistic回归模型的经验似然诊断

经验似然方法已经被广泛用于许多模型的统计推断.基于经验似然对Logistic回归模型进行统计诊断.首先给出模型的估计方程,进而得到模型参数的极大经验似然估计;其次,基于经验似然研究了三种不同的影响曲率;最后通过实例分析,说明了统计诊断方法的有效性.     

上证股指极值模型估计和VaR计算

POT极值模型参数的准确估计是计算金融资产回报厚尾分布市场风险的关键.由n阶概率加权矩得到参数的二项式回归估计,而将参数的零,一阶概率加权矩估计予以推广.极大似然估计中.将极大化似然函转化为二元函数无条件极值问题·其他参数估计方法的结果作为迭代的初始值,通过它们的似然函数值和极大似然函数值的比较以及迭代次数判断方法的优劣.实证研究表明:参数的零、一阶概率加权矩估计较接近于真值,随着阶数的提高,二项式回归参数估计的误差很大.参数的极大似然估计优于非线性回归估计优于零、一阶概率加权矩估计.在此基础上计算上证A股指数vaR值.     

中国银行间拆借利率扩散模型的极大拟似然估计

本文用极大拟似然估计法估计了中国银行间市场七天拆借利率扩散模型的参数。并用自助法对众多不同的模型进行了广义拟似然比检验。结论表明:中国货币市场利率具有均值回复效应:利率敏感系数γ值为1.421265,对利率水平具有较高敏感性。     

Testing for Constant Nonparametric Effects in General Semiparametric Regression Models with Interactions

We consider the problem of testing for a constant nonparametric effect in a general semiparametric regression model when there is a potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey 1-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.     

泊松回归模型中协变量含误差的经验似然推断

在模型的协变量含有测量误差的情况下,考虑一类泊松回归模型的统计推断问题.通过巧妙地构造辅助随机向量,提出一个工具变量类型的经验似然统计推断方法.证明构造的经验对数似然比函数渐近服从标准卡方分布,进而给出了回归系数的置信区间.所提出的估计方法可以有效地消除测量误差对估计精度的影响,并且具有较好的有限样本性质.     

Efficient Algorithm for Computing Maximum Likelihood Estimates in Linear Transformation Models

Linear transformation models, which have been extensively studied in survival analysis, include the two special cases: the proportional hazards model and the proportional odds model. Nonparametric maximum likelihood estimation is usually used to derive the efficient estimators. However, due to the large number of nuisance parameters, calculation of the nonparametric maximum likelihood estimator is difficult in practice, except for the proportional hazards model. We propose an efficient algorithm for computing the maximum likelihood estimates, where the dimensionality of the parameter space is dramatically reduced so that only a finite number of equations need to be solved. Moreover, the asymptotic variance is automatically estimated in the computing procedure. Extensive simulation studies indicate that the proposed algorithm works very well for linear transformation models. A real example is presented for an illustration of the new methodology.     

基于市场债券报价的利率模型参数估计与比较

分别基于短期利率期限结构延拓Vasicek与CIR模型,提出了一种有效的正则化参数估计计算方法.方法将通过当前交易市场中不同期限的零息债券市场报价来实现对于时间函数的参数估计.数值试验表明了参数估计方法的稳定性.     

Statistical Inference for the Parameter of Pareto Distribution Based on Progressively Typ e-I Interval Censored Sample

In this paper, the estimation of parameters based on a progressively typeI interval censored sample from a Pareto distribution is studied. Different methods of estimation are discussed, which include mid-point approximation estimator, the maximum likelihood estimator and moment estimator. The estimation procedures are discussed in details and compared via Monte Carlo simulations in terms of their biases.     

Uniform Convergence Rate of Estimators of Autocovariances in Partly Linear Regression Models with Correlated Errors

Consider the partly linear regression model ,where yi's are responses, xi = (xi1, xi2,…,xip)' and ti ∈T are known and nonrandom design points, T is a compact set in the real line is an unknown parameter vector, g(·) is an unknown function and {Ei} isa linear process, i.e., random variables with zeromean and variance o2e. Drawing upon B-spline estimation of g(·) and least squares estimation of 0, we construct estimators of the autocovariances of {Ei}- The uniform strong convergence rate of these estimators to their true values is then established. These results not only are a compensation for those of [23], but also have some application in modeling error structure. When the errors {Ei} are an ARMA process, our result can be used to develop a consistent procedure for determining the order of the ARMA process and identifying the non-zero coefficients of the process. Moreover, our result can be used to construct the asymptotically efficient estimators for parameters in the ARMA error process.     

利率衍生品定价可行性模型

众所周知,Vasicek短期利率模型,由于可取负的利率,使得利率衍生物定价计算具有不稳定现象,并引起业界对它的定价的可信度产生怀疑.该文指出只需以息票作为新的计价单位（Benchmark）,利率衍生物定价计算不稳定现象就可避免,为了说明定价的可行性,将在随机利率条件下以欧式看涨期权为例,通过数值方法对Vasicek和CIR这两类利率模型衍生物定价的误差进行分析.     

基于局部多项式展开的多元非参数模型贝叶斯带宽选择

在多元非参数模型中带宽和阶的选择对局部多项式估计量的表现十分重要。本文基于交叉验证准则提出一个自适应贝叶斯带宽选择方法。在给定的误差密度函数下,该方法可推导出对应的似然函数,并构造带宽参数的后验密度函数。随后,通过带宽的后验期望可同时获得阶和带宽的估计。数值模拟的结果表明,该方法不仅比大拇指准则方法精确,且比交叉验证方法耗时更少。与此同时,与Nadaraya-Watson估计相比,所提带宽选择方法对多元非参数模型的适应性要更好。最后,本文通过一组实际数据说明有限样本下所提贝叶斯带宽选择的表现很好。