具有结构变化的非线性回归模型的阶段异方差检验

对于具有结构变化的非线性回归模型,两阶段的随机误差同时具有方差齐性是一个基本假设,但是该假设未必正确．本文研究该模型阶段异方差的检验问题．首先探讨了两阶段异方差的同时检验,然后构造了两阶段异方差的两个单个检验,分别得到了同时检验和单个检验的score统计量以及相应的调整形式．然后应用得到的检验统计量分析了南澳大利亚洋葱数据的阶段异方差性(Ratkowsky,1983),并用AIC,SBC进行模型比较,得到的结果与检验结果非常吻合．最后,用Monte Carlo模拟方法研究了统计量的检验功效．     

非线性纵向数据模型中方差和自相关系数的齐性检验

刻画纵向数据的协方差结构有三个可能因素：随机效应、序列相关和随机误差．在纵向数据分析中，模型方差的齐性是一个基本假定．但是，该假设未必正确．Zhang和、Weiss^[1]研究了具有随机效应的线性模型的异方差检验．林金官和韦博成^[2]将Zhang和、Weiss^[1]的结果推广到非线性情形．本文对具有自相关误差的非线性纵向数据模型，研究了方差齐性和相关系数的齐性检验，得到了检验的score统计量并应用于血浆渗透数据(见Davidian和Giltian^[3])．最后，本文还给出了模拟结果．     

Heteroscedasticity check in nonlinear semiparametric models based on nonparametric variance function

The assumption of homoscedasticity has received much attention in classical analysis of regression. Heteroscedasticity tests have been well studied in parametric and nonparametric regressions. The aim of this paper is to present a test of heteroscedasticity for nonlinear semiparametric regression models with nonparametric variance function. The validity of the proposed test is illustrated by two simulated examples and a real data example.     

Two tests for heteroscedasticity in nonparametric regression

In this paper, two new tests for heteroscedasticity in nonparametric regression are presented and compared. The first of these tests consists in first estimating nonparametrically the unknown conditional variance function and then using a classical least-squares test for a general linear model to test whether this function is a constant. The second test is based on using an overall distance between a nonparametric estimator of the conditional variance function and a parametric estimator of the variance of the model under the assumption of homoscedasticity. A bootstrap algorithm is used to approximate the distribution of this test statistic. Extended versions of both procedures in two directions, first, in the context of dependent data, and second, in the case of testing if the variance function is a polynomial of a certain degree, are also described. A broad simulation study is carried out to illustrate the finite sample performance of both tests when the observations are independent and when they are dependent.     

具有随机效应和AR(1)误差的非线性纵向数据模型中组间方差和自相关系数的齐性检验

组间方差和自相关系数的齐性是纵向数据分析的基本假设之一,然而这种假设需要进行统计检验. Zhang \&; Weiss$^{[15]}$ 讨论了线性随机效应模型的组间和组内方差齐性的检验问题;林金官 \&; 韦博成$^{[10]}$ 研究了具有AR(1)误差但没有随机效应的非线性模型的自相关系数的齐性检验.该文研究具有随机效应和AR(1)误差的非线性模型的组间方差和自相关系数的齐性检验问题,构造了几个score检验统计量, 并通过Monte Carlo模拟方法研究了检验统计量的性质.最后利用该文的方法分析一组实际数据和一组模拟数据.     

带干扰风险的破产模型的研究

本文对古典风险模型中保险公司按单位时间常数率收到保险费的假设做了改进,将每次收到的保险费的次数看作是复合泊松过程,将每次收到的保费和每次的理陪额均看作是服从指数分布的随机变量,并引入带干扰风险的扰动项,从而对古典风险模型进行推广,且给出了相应的破产概率上界,分析了破产概率的上界与准备金,索赔额,净保费和扰动方差之间的关系。     

带干扰风险的破产模型的研究

本文对古典风险模型中保险公司按单位时间常数率收到保险费的假设做了改进,将每次收到的保险费的次数看作是复合泊松过程,将每次收到的保费和每次的理陪额均看作是服从指数分布的随机变量,并引入带干扰风险的扰动项,从而对古典风险模型进行推广,且给出了相应的破产概率上界,分析了破产概率的上界与准备金,索赔额,净保费和扰动方差之间的关系.     

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??The Bayes estimators of variance components are derived under weighted square loss function for the balanced one-way classification random effects model with the assumption that variance component has the conjugate prior distribution. The superiorities of the Bayes estimators for variance components to traditional ANOVA estimators are studied in terms of the mean square error (MSE) criterion. Finally, a remark for main results is given.     

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.     

具有一致相关的纵向数据模型中方差和相关系数的齐性检验

在纵向数据分析中, 模型方差的齐性是一个基本假定, 但是该假定未必正确. 林金官、韦博成^[1]讨论了具有AR(1)误差的非线性纵向数据模型中方差和相关系数的齐性检验. 本文对具有一致相关协方差结构的纵向数据模型, 研究了方差齐性和相关系数齐性的检验, 得到了检验的score统计量, 并应用于葡萄糖数据. 最后, 本文还给出了模拟结果.     

Exact tests in simple growth curve models and one-way ANOVA with equicorrelation error structure

We consider exact tests with several equicorrelation error structures and combination of equicorrelation covariance structures in simple growth curve model having single or multiple treatments and in one-way ANOVA model. Exact inferences using generalized p-values are obtained. Tests for equal treatment effects under equal equicorrelation error term and for unequal equicorrelation error terms are also developed. Two examples are given to illustrate the importance of our results. According to our findings, we would be better off dropping the assumption of equal variance when the heteroscedasticity is serious. Therefore, tests based on generalized p-values without the assumption of equal variance are much more powerful than tests with this assumption.     

On the link between credibility and frequency premium

This paper questions the equidistribution assumption for the random effects in a frequency risk model. Two models are presented, which use parametric and nonparametric links between the variance of the random effect and frequency risk. They are estimated on a Spanish automobile insurance portfolio, for which a decreasing link is obtained. Conclusions are drawn for credibility and bonus-malus coefficients.     

Heteroscedasticity checks for regression models

For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.     

非线性纵向数据模型中自相关性和随机效应的存在性检验

刻画纵向数据协方差结构有三种可能因素 ,即序列相关 (特别是一阶自相关 )、随机效应和常规的随机误差 (Diggleetal,2 0 0 2 ) .本文研究非线性纵向数据模型的自相关性和随机效应存在性的单个和联合检验 ,得到了检验的score统计量 ,并利用血浆药物渗透数据 (Davidian&Gilinan ,1 995)说明检验方法的应用 .     

Bayesian Multivariate Distributional Regression With Skewed Responses and Skewed Random Effects

The normal and the t distribution are classical tools for building random effects regression models where both can be used for the specification of either the conditional response distribution or the random effects distribution. However, the underlying assumption of symmetry can be questionable in many applications. We, therefore, propose regression models where the skew-normal and skew-t distribution are considered for both the response and the random effects specification and embed these models in the framework of distributional regression such that regression predictors can be specified for all distributional parameters. The distributional regression framework also allows us to consider multivariate versions of the skew-normal and the skew-t distribution. For Bayesian inference, we adapt iteratively weighted least-square proposals within Markov chain Monte Carlo simulations such that they can also facilitate the inclusion of nonnormal random effects specifications. Model choice is based on the Watanabe–Akaike information criterion, in particular, to differentiate between skew and nonskew distributional specifications in a number of simulation studies. Finally, to illustrate their practical applicability, the developed models are applied to a study on cholesterol levels originating from the Framingham Heart Study and a dataset from the Demographic and Health Surveys on undernutrition among children in Nigeria. Supplementary material for this article is available online.     

非线性随机效应模型的异方差性检验

随机效应模型广泛应用于刻画重复测量数据的特征．在该模型中,随机误差的方差包括受试群体内部及受试群体之间两项方差．Ｚｈａｎｇ和 Ｗｅｉｓｓ ２０００年研究了线性随机效应模型的异方差检验,本文对非线性随机效应模型,分别讨论了群体内、群体间和多变量的异方差性的检验问题,得到了检验的ｓｃｏｒｅ统计量,并讨论了三种情形下,相应的ｓｃｏｒｅ函数之间的关系．最后给出一个数值例子说明上述方法的有用性．     

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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Panel数据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.通过模拟给出检验的功效和置信区间的覆盖率. 模拟结果表明,广义p值理论方法应用于含有冗余参数的Panel数据模型参数检验问题是灵活而有效的.     

Estimation of moments for linear panel data models with potential existence of time effects

In econometric analysis of panel data, one always doesn’t have enough information to assure the existence/absence of time effects, which can lead to wrong conclusions in statistical inference such as moment estimation and hypothesis testing. In this paper, estimation of second and fourth order moments of the individual effects and the errors are studied for linear panel data models without information on the existence/absence of time effects. With differences of the residuals over the individual index, the orthogonality-based moment estimators of the random individual effects and the errors are respectively obtained without affecting each other. These moment estimators are robust on the potential existence of time effects. Their asymptotic normalities are obtained under some moment conditions. Monte Carlo simulations are carried out for illustration.     

Random Features of the Fatigue Limit

The classical fatigue limit is often an important characteristic in fatigue design regarding metallic material. The limit is usually obtained from a staircase test in combination with some assumption about the statistical distribution of the limit. This distribution can be of a normal, log-normal or of extreme value type and no particular physical argument gives favor to any specific distribution. This leads to a certain ambiguity in the evaluation of test results which forces the designer to introduce large safety factors. In order to find a physically based statistical distribution for use in staircase tests to determine the fatigue limit we present here a random model for the fatigue limit based on the following assumptions; (i) The square root area model according to Murakami and co-workers is valid, (ii) the randomness in the fatigue limit is induced by the randomness of the maximum defect size, (iii) the random maximum defect size has an extreme value distribution of Gumbel type. This leads to the fatigue limit distribution based on Gumbel (FLG), which is recommended to replace the normal distribution in the evaluation of staircase fatigue tests in case of hard materials. It turns out that the skewness of the resulting distribution depends on the coefficient of variation; with a normal-like non-skewed distribution at the coefficient of variation of five percent.     

Likelihood and conditional likelihood inference for generalized additive mixed models for clustered data

Lin and Zhang (J. Roy. Statist. Soc. Ser. B 61 (1999) 381) proposed the generalized additive mixed model (GAMM) as a framework for analysis of correlated data, where normally distributed random effects are used to account for correlation in the data, and proposed to use double penalized quasi-likelihood (DPQL) to estimate the nonparametric functions in the model and marginal likelihood to estimate the smoothing parameters and variance components simultaneously. However, the normal distributional assumption for the random effects may not be realistic in many applications, and it is unclear how violation of this assumption affects ensuing inferences for GAMMs. For a particular class of GAMMs, we propose a conditional estimation procedure built on a conditional likelihood for the response given a sufficient statistic for the random effect, treating the random effect as a nuisance parameter, which thus should be robust to its distribution. In extensive simulation studies, we assess performance of this estimator under a range of conditions and use it as a basis for comparison to DPQL to evaluate the impact of violation of the normality assumption. The procedure is illustrated with application to data from the Multicenter AIDS Cohort Study (MACS).