加权非线性随机系数模型异方差性的Score检验

在回归分析中，随机误差的方差齐性的假设往往有助于问题的解决，但方差齐性假设并不总是正确的。在线性和非线性回归中关于异方差的诊断问题已有许多讨论，在韦博成（1995）讨论的加权非线性回归模型的基础上，用随机系数的方法，讨论加权线性随机系统模型中的异方差检验问题，得到了方差齐性检验的Score统计量。     

非线性回归模型相关性和异方差性的检验

本文讨论了误差是一阶自回归序列的非线性回归模型，首先导出了关于误差相关性和异方差性的似然比检验统计量和Ｓｃｏｒｅ检验统计量，随后利用参数正交变换，得到了修正的似然比检验统计量及修正的Ｓｃｏｒｅ检验统计量。此外，当误差项是一阶滑动平均序列时相应的检验问题也作了讨论。     

残差控制图应用中的自相关过程模型研究

随着自动化技术的发展,数据自相关现象在现代制造业中普遍存在。基于自相关模型的残差控制图是解决自相关数据统计监控问题的一类较好方法。现有研究均假设数据呈一阶自相关,仅研究一阶自回归模型与残差控制图结合的过程监控问题。但是,实际生产中观测数据可能服从多阶自相关。基于此,针对数据多阶自相关的过程监控问题,运用蒙特卡洛仿真法,研究不同自回归阶数的自回归模型对残差控制图性能的影响。研究表明:在残差控制图应用中,过程受控时,一阶自回归模型的表现与同阶自回归模型的性能表现相当,即二者发生第一类错误的概率相差不大;而在质量偏移诊断中,一阶自回归AR(1)模型的性能表现整体优于同阶的自回归模型,大大降低了漏警的成本。     

相依误差下异方差非参数回归模型的样条估计

一些经济金融等实际数据中含有非线性趋势、异方差和相依关系,固定设计和相依误差下的异方差非参数回归模型因其能够反映这些数据特征而有着重要的应用．样条方法是常用的非参数光滑方法之一．为了探究样条方法在这类模型中的可用性,本文在$\alpha$- 混合条件下,讨论了均值函数和方差函数的多项式样条估计的逐点相合性,得到了逐点收敛速度．此外,还对所讨论的方法进行了数值模拟,结果表明样条方法在这类模型的应用中是可行的．     

异方差随机系数回归模型的最优设计

最优设计方法在工程技术领域和工农业生产中具有广泛的应用．随机系数模型的最优设计研究中通常假定随机误差项具有相同的方差,实际中误差的产生往往与观测点有关,从而具有异方差性质．本文研究一般闭区间设计域上异方差随机系数回归模型的最优近似设计问题．我们获得了最优设计可以在设计域的两个端点处得到的一组充分条件,并进一步证明了当误差项方差具有对称结构且设计域是对称区间时,设计域两个对称端点处的等权重设计同时具有多重最优性质,这时最优设计不依赖于模型中随机误差项的方差结构及随机系数项的方差．     

半参数回归模型拟极大似然估计的弱相合性

本文考虑一类固定设计的半参数回归模型,其误差为一阶自回归时间序列。用权函数及拟极大似然估计方法得到了一些参数及非参数的拟极大似然估计量,在适当的条件下,研究了它们的弱相合性,从而丰富了该类半参数回归模型的估计理论与方法。     

基于GARCH模型MSVM的轴承故障诊断方法

针对振动信号因非平稳性导致自回归(AR)模型无法有效描述信号特征的不足,提出一种基于广义自回归条件异方差(GARCH)模型多类支持向量机(MSVM)的故障诊断方法。该方法首先利用GARCH模型拟合各种故障信号,将所得模型参数作为故障诊断特征,以MSVM作为故障诊断方法。试验结果验证了GARCH模型方法的可行性和有效性,同时将该方法同基于AR模型的方法及其改进方法进行比较,结果表明该方法在诊断率及诊断时间上都有明显提高。     

局部线性回归模型的半参估计

针对局部线性估计方法收敛速度较慢且对窗宽选择不稳健的问题,本文提出一种改进的局部线性半参估计方法。首先,选择不同窗宽作相应的局部线性估计,然后利用这些估计构造参数回归模型,由此给出回归函数的参数估计。相对于局部线性估计,新方法在不改变方差阶的情况下,将估计偏差的阶由h2减小至h4,最优均方收敛速度提高至O(n?89),且对窗宽选择稳健。模拟研究验证了新方法的有效性。     

具有非参数AR(1)误差的回归模型的局部线性估计

考虑随机设计下具有一阶非参数自回归误差的线性回归模型,构造了参数和非参数函数的局部线性估计。在适当的条件下,证明了参数估计量的渐近正态性,并给出了非参数函数估计的收敛速度。模拟算例表明局部线性方法优于核方法。     

变系数空间自回归模型的Bootstrap检验

变系数空间自回归模型是变系数模型在空间数据分析方面的推广,因其众多的应用背景而得到广泛的重视和研究,确认模型中系数是否真正随变量的变化而变化是应用变系数空间自回归模型需解决的首要问题.本文基于Bootstrap检验方法研究了变系数空间自回归模型中的常系数项的辨别问题,为建立半变系数空间自回归模型提供依据.最后,通过模拟...     

Monitoring simple linear profiles in the presence of generalized autoregressive conditional heteroscedasticity

In this paper, monitoring of simple linear profiles is investigated in the presence of nonequality of variances or heteroscedasticity, ie, generalized autoregressive conditional heteroscedasticity. In this condition, using of the common methods regardless of the heteroscedasticity leads to the fault interpretations. We consider a simple linear profile and assume that there is a generalized autoregressive conditional heteroscedasticity (GARCH) (1,1) model within the profiles. Here, we particularly focus on Phase II monitoring of simple linear regression. We studied the generalized autoregressive conditional heteroscedasticity effect, briefly GARCH effect, on the average run length criterion. As the remedial measures, the weighted least squares method to estimate the regression parameters and the heteroscedasticity‐consistent approaches to estimate the covariance matrix of regression parameters, are used to extract the GARCH effect. Two control chart methods namely T² and exponentially weighted moving average 3 are discussed to monitor the simple linear profiles. Their performances are evaluated by using the average run length criterion. Finally, a real case from an industry field is studied.     

Diagnostics for Nonlinearity and Heteroscedasticity in Errors-in-Variables Regression

We suggest new plotting methods for residual analysis in errors-in-variables regression. The standard residuals analyses are based on the methods of Miller and Fuller and are appropriate when the errors in the regression and the measurement error are symmetrically distributed. By “appropriate,” we mean that in large samples the plots will not falsely identify a nonexistent pattern of heteroscedasticity or nonlinearity. The standard methods are not appropriate in this sense for skewed error distributions. Our methods require replication of the error-prone predictors, but they are appropriate for both symmetric and skewed error distributions. Besides residual plots, we also construct hypothesis tests for heteroscedasticity. In terms of power for detecting heteroscedasticity, we show that the standard plot is more efficient when the residuals are normally distributed, although it does not achieve its nominal level for skewed error distributions. Simulations are used to illustrate the results. We also consider the case that measurement error in the response is correlated with the measurement error in the predictors, suggesting new residual plots in this setting. The article also contains a short summary of plotting techniques for detecting heteroscedasticity in regression.     

A central limit theorem for sums of functions of residuals in a high-dimensional regression model with an application to variance homoscedasticity test

We establish a joint central limit theorem for sums of squares and the fourth powers of residuals in a high-dimensional regression model. We then apply this CLT to detect the existence of heteroscedasticity for linear regression models without assuming randomness of covariates when the sample size n tends to infinity and the number of covariates p may be fixed or tend to infinity.     

Bayesian approach to the analysis of fluorescence correlation spectroscopy data I: theory

Fluorescence correlation spectroscopy (FCS) is a powerful tool to infer the physical process of macromolecules including local concentration, binding, and transport from fluorescence intensity measurements. Interpretation of FCS data relies critically on objective multiple hypothesis testing of competing models for complex physical processes that are typically unknown a priori. Here, we propose an objective Bayesian inference procedure for testing multiple competing models to describe FCS data based on temporal autocorrelation functions. We illustrate its performance on simulated temporal autocorrelation functions for which the physical process, noise, and sampling properties can be controlled completely. The procedure enables the systematic and objective evaluation of an arbitrary number of competing, non-nested physical models for FCS data, appropriately penalizing model complexity according to the Principle of Parsimony to prefer simpler models as the signal-to-noise ratio decreases. In addition to eliminating overfitting of FCS data, the procedure dictates when the interpretation of model parameters are not justified by the signal-to-noise ratio of the underlying sampled data. The proposed approach is completely general in its applicability to transport, binding, or other physical processes, as well as spatially resolved FCS from image correlation spectroscopy, providing an important theoretical foundation for the automated application of FCS to the analysis of biological and other complex samples.     

Further Consideration of Heteroscedasticity in Fitting Kinetic Models

Box and Hill [6] recently proposed a method for using power transformation weighting in least squares analysis to account for changing variance. Such an approach can be useful when the original data are heteroscedastic but adequate weight estimates are not available, and when the original data are homoscedastic but heteroscedasticity is induced by the data analyst in linearising a nonlinear model.

Several aspects of their proposal are examined for practical implications in fitting chemical kinetic models and a more direct algorithm is recommended for fitting nonlinear models to heteroscedastic data. Methods for testing model adequacy and assessing parameter precision in such situations are also discussed.     

Efficient estimation for marginal generalized partially linear single-index models with longitudinal data

We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations (GEE)-based approach is proposed to estimate unknown regression parameters. Within a wide range of bandwidths for estimating the nonparametric function, our profile GEE estimator is consistent and asymptotically normal even if the covariance structure is misspecified. Moreover, if the covariance structure is correctly specified, the semiparametric efficiency can be achieved under heteroscedasticity and without distributional assumptions on the covariates. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. The proposed methodology is further illustrated through a data analysis.     

Modelling time-series of British road accident data

The paper reports analyses of road accident data in Britain in which time series of monthly accident data for the period 1970-1978 have been related to a number of explanatory variables. Two sets of results are presented. Two-vehicle accidents were modelled by regression; because the time trends in this data appeared to be reasonably consistent, the resulting model was regarded as adequate. In the case of single-vehicle accidents, trends were not consistent over the period, and it was considered that the Box-Jenkins time series method might be more appropriate than simple regression. The principles involved in fitting Box-Jenkins models to this data are explained and the results compared with the regression method. The tentative conclusion drawn from this comparison is that because accident series are generally very "noisy" and autocorrelation among the residuals from standard regressions not very strong, Box-Jenkins models are unlikely to represent the series appreciably better than regression based on the assumption of uncorrelated residuals. Predictions of the alternative models for the years 1979-1981 are presented and discussed.     

ADMM for High-Dimensional Sparse Penalized Quantile Regression

Sparse penalized quantile regression is a useful tool for variable selection, robust estimation, and heteroscedasticity detection in high-dimensional data analysis. The computational issue of the sparse penalized quantile regression has not yet been fully resolved in the literature, due to nonsmoothness of the quantile regression loss function. We introduce fast alternating direction method of multipliers (ADMM) algorithms for computing the sparse penalized quantile regression. The convergence properties of the proposed algorithms are established. Numerical examples demonstrate the competitive performance of our algorithm: it significantly outperforms several other fast solvers for high-dimensional penalized quantile regression. Supplementary materials for this article are available online.     

Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario

This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car–car crashes, and sideswipe crashes have opposite effects between car–car and truck–truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity.