连续型半参数广义线性纵向数据模型的方差成分检验

回归模型的方差成分检验是一个非常重要的问题.该文针对离差参数的变异, 随机效应的影响及两者同时具有的三种情形, 研究了基于纵向数据的连续型半参数广义线性模型的方差成分检验, 得到了Score检验统计量, 最后通过计算机模拟验证了该文所提出的方法的有效性.     

离散型半参数广义线性纵向数据模型的方差成分检验

在回归分析中, 随机误差是否存在方差非齐性是大家十分关心的问题, 本文根据Laplace展开原理针对随机效应的影响研究了基于纵向数据的离散型半参数广义线性模型的方差成分检验,得到了Score检验统计量, 最后通过一个实例和计算机模拟验证了本文所提出的方法的有效性.     

单指标模型异方差检验（英文）

在可加回归模型中,高维回归分析一般采用单指标模型.该模型与参数模型相比更加灵活,同时避免了维数灾难,因为单指标将标准变量向量的维数降低为单变量指标.本文构建了一个带有函数型误差项的单指数回归模型用于检验单指标模型的异方差性.由于回归模型的有效推断要求在存在异方差的情况下考虑异方差,本文提出了检验单指标模型方差不变性的假设.将Levene检验和无限因子水平的方差分析理论结合得到检验统计量用来评估方差同质性.模拟研究显示与已有方法相比,所提检验统计量适用于多种情形.最后将本文的方法应用于分析一组实际数据.     

单指标回归模型的若干检验问题

在参数和非参数回归模型中,关于异方差检验、一阶自相关存在性检验以及附加变量检验问题都有很多的研究.文中利用P-样条方法,研究了单指标模型的异方差、一阶自相关存在性及附加变量检验问题,分别得到了三种情况下的Score检验统计量,最后给出计算机模拟和实际例子,证实了文中所提出的方法的可行性和有效性,推广和发展了先前的工作.     

线性混合模型方差分量的检验

对于含有两个方差分量的随机效应设计阵为任意阵的线性混合模型的方差分量单边检验问题给出了精确的F检验和基于广义p值的检验.对于给出的精确的F检验给出检验存在条件以及是一致最优无偏检验的条件.通过数值模拟,基于广义p值检验的功效和犯第一类错误的概率被讨论,由模拟结果可以看出基于广义p值的检验很好地控制了犯第一类错误的概率.     

线性混合模型中方差分量的广义推断

本文考虑了线性混合模型中方差分量的假设检验和区间估计问题.基于广义P-值和广义置信区间的概念,构造了对应于随机效应的单个方差分量的精确检验和置信区间.所构造的广义p-值和广义置信区间是最小充分统计量的函数.对于两个独立线性混合模型中对应于随机效应的方差分量的比较,建立了精确检验和置信区间.进-步,研究了所给检验和置信区间的统计性质,给出了这些检验方法与文献中已有方法的功效比较的模拟结果.模拟结果表明,新检验在功效方面有显著的改进.最后,通过-个实例来演示本文方怯.     

偏正态混合效应模型中方差分量函数的参数Bootstrap推断

偏正态混合效应模型通过引入偏度参数,以便更好地刻画实际数据偏态特征,所以被广泛应用于众多实际领域.进一步,方差分量的假设检验一直是该模型的热点研究问题.因此,有必要在偏正态分布下系统讨论混合效应模型中方差分量函数的统计推断问题.首先,分别基于参数Bootstrap方法和广义方法探讨单个方差分量、方差分量之和、方差分量之比的单边假设检验和区间估计问题.其次,Monte Carlo结果表明,在所给样本量和参数设置下,参数Bootstrap方法大多数情况下优于广义方法.最后,将上述方法应用于空气质量指数的案例研究中,以验证所给方法的合理性与有效性.     

偏正态非平衡面板单因素随机效应模型的Bootstrap推断

针对偏正态非平衡面板单因素随机效应模型,文章研究了回归系数和方差分量函数的假设检验和区间估计问题.首先,基于矩阵分解技术,给出回归系数的精确检验方法.其次,利用Bootstrap方法和广义方法,构造单个方差分量、方差分量之和的检验统计量和置信区间.再次,建立方差分量之比的精确检验和近似检验.文章证明了所给检验方法和置信区间的变换不变性等理论性质.Monte Carlo结果表明,对于所设参数和样本量,文章所给方法在犯第一类错误的概率和功效意义下,具有统计优良性.最后,将上述方法应用于汽油消耗量的案例分析.     

随机权函数半参数回归模型的导方差统计分析

在回归分析中,观测值的方差齐性只是一个基本的假定,在参数、半参数和非参数回归模型中关于异方差检验和估计问题已有很多研究.本文在冉昊和朱忠义(2004)讨论的半参数回归模型的基础上,用随机参数方法,讨论随机权函数半参数回归模型中的异方差检验问题,得到了方差齐性检验Score统计量,同时,当半参数模型存在异方差时,本文还给出了估计方差的方法.     

半参数随机效应模型的异方差检验

在许多实际问题中,检验观察数据是否出现异方差性是一个相当感兴趣的问题.该文研究了半参数随机效应模型的异方差检验问题.基于Lin(1997)的方法,得到了检验方差成分都为零的Score检验统计量.通过随机模拟和实际数值例子,论证了方法的有效性.利用现有的统计软件,容易实现该文所提出的检验方法.     

A choice probability characterization of generalized extreme value models

A set of necessary and sufficient conditions is established for the representability of choice probabilities by additive random utility models with generalized extreme value (GEV) distributions of utilities. These conditions yield an operational testing procedure for GEV-representability which does not require explicit construction of the underlying distribution of utilities. In addition, this characterization of GEV models reveals a number of their underlying behavioral features.     

Interpolation solution in generalized stochastic exponential population growth model

In this paper, first we consider model of exponential population growth, then we assume that the growth rate at time t is not completely definite and it depends on some random environment effects. For this case the stochastic exponential population growth model is introduced. Also we assume that the growth rate at time t depends on many different random environment effect, for this case the generalized stochastic exponential population growth model is introduced. The expectations and variances of solutions are obtained. For a case study, we consider the population growth of Iran and obtain the output of models for this data and predict the population individuals in each year.     

Long term behaviors of stochastic single-species growth models in a polluted environment

Thresholds for extinction and persistence are important for assessing the risk of mortality in systems exposed to toxicant. In this paper, three single-species models with random perturbation in a polluted environment are proposed and investigated. One is the generalized logistic model and the other two are the stochastic resource–consumer models of Leslie and Gallopin. For each model, the survival threshold is obtained in some cases. In general, each threshold is determined by intensity of the random noise, the mean stress measure in organisms, the population intrinsic growth rate and the stress response rate.     

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.     

TFR、TRV和CE模型序加试验下WEIBULL分布产品的失效分布

本针对TFR模型，首次提出将步加试验推广至序加试验，就两参数Weibull分布给出了损伤因子函数，同时给出了产品寿命的残存函数，另外针对TRV模型，在序加试验下就两参数Weibull分布给出了损伤系数，同时给出了产品寿命的残存函数。     

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本文利用广义p-值和广义置信区间的概念构造 含有三个随机效应的套误差分量模型中方差分量的几种新的精确检验和置信区间, 并讨论它们在尺度变换下的不变性. 模拟结果表明, 基于广义p-值的检验很好地控制了犯第一类错误的概率.     

On the safety stock problem for random delivery processes

Inventory models are considered in which the delivery of an order occurs not on one occasion but at random moments of a period in random parts. We give two extensions of the reliability type inventory model of A. Prékopa. In this model a known constant demand rate is assumed and a simple approximate formula is given for the initial stock of the order period which serves as safety stock and ensures a continuous supply during the whole order period on a prescribed probability level. This formula is widely used in practice for safety stock planning in the case when deliveries in random parts occur.We formulate a generalized version of the random delivery process and derive the exact solution of the safety stock which can be applied also for the previous model. In the second model a random demand rate is considered together with a random delivery process. An exact solution method and a simple approximate formula for the safety stock will be discussed. We have experiences in the application of these models both in a steel works and a textile factory in Hungary.     

戈德菲尔德匡特检验的推广

在大多数经济现象中,回归模型的随机扰动项并不具有同方差性,它可能随观察值的不同而变化。对这种异方差模型进行最小二乘估计,会产生严重的后果,因此研究异方差的检验方法具有重要意义。由于戈德菲尔德 匡特检验方法只适用于一个自变量,因此,本文对G Q检验进行了推广,说明在多变量的情况下,可以利用主成分对样本数据进行排序,从而解决了对多变量数据的排序问题,使戈德菲尔德 匡特异方差检验得到了推广,并举实例说明。