偏正态数据下众数混合专家回归模型的参数估计

混合专家模型是对异质总体数据进行回归、分类和聚类的异构性建模的流行框架.研究基于偏正态分布,提出了众数混合专家回归模型,该模型既对混合偏态数据分类后进行众数建模,同时又对混合比例建模,相比单纯的众数回归模型具有更大的适应性,可以概括和描述众多的实际问题.采用了一种有效的模式识别聚类方法来选择子聚类的数量.分别应用MM算...     

测量误差数据下偏正态众数回归模型的参数估计

现有对回归模型的研究大多仅限于直接观测的解释变量,忽略数据的测量误差将增加模型参数的估计偏差.目前关于测量误差模型的研究主要集中在回归误差服从正态分布的假设,这种假设不适用于研究非对称的数据.对于偏斜数据,众数的代表性优于均值和中位数.本文基于测量误差数据介绍了偏正态众数回归模型,并通过EM算法估计了模型的参数.模拟研究的结果表明,协变量带测量误差下的众数回归比均值回归有更好的表现.通过实例分析进一步表明了所提出模型和方法的有效性.     

偏正态数据下众数回归模型的参数估计及应用

本文针对金融、经济、社会科学、环境科学、工程技术和生物医学等研究领域存在的不对称数据,提出偏正态数据下众数回归模型,基于牛顿-拉弗森迭代利用EM算法来估计未知参数。通过Monte Carlo模拟和BMI数据实例分析验证,表明本文所提出方法的有效性,对于偏正态数据众数回归模型的估计效果优于均值回归模型。     

基于偏正态数据下位置、均值回归模型的参数估计

偏正态分布是正态分布的扩展,它不仅具有正态分布的性质也有偏态分布的性质,在实际应用中比正态分布适用性更广泛.本文扩展了偏正态数据下的位置、均值回归模型,讨论了偏正态数据下位置、均值回归模型的参数估计,本文使用的偏正态分布是Sahu等(2003)提出的偏正态分布,相较于Azzalini(1986)提出的偏正态分布,其对于...     

偏正态数据下混合非线性位置回归模型的统计诊断

在经济、生物医学、环境科学等领域存在着这样一类混合数据,非对称、非线性并且还含有异常点或强影响点,如果只是简单的对总体数据进行诊断,得到的结果可能不精确.因此研究了偏正态数据下混合非线性位置回归模型的统计诊断,对混合数据总体不分类做诊断与分类后再做诊断相比较,发现分类后做诊断结果更精确.其次,将Pena距离推广到了偏正...     

基于Pena距离的偏正态数据下位置回归模型的统计诊断

Pena距离是研究偏态数据的一种有用工具.本文利用Pena距离研究了偏正态数据下位置回归模型的统计诊断问题,得到了位置回归模型下Pena距离的表达式,对其性质进行讨论,从而得到高杠杆异常点的判别方法. Pena距离与Cook距离、似然距离进行比较,得到在一定的条件下Pena距离优于Cook、似然距离.通过随机模拟试验研究和实例分析,表明本文提出的理论和方法是科学合理的.     

带缺失数据的偏正态众数回归模型的参数估计

针对现实生活中大量数据存在偏斜的情况,构建偏正态数据下的众数回归模型.又加之数据的缺失常有发生,采用插补方法处理缺失数据集,为比较插补效果,考虑对响应变量随机缺失情形进行统计推断研究.利用高斯牛顿迭代法给出众数回归模型参数的极大似然估计,比较该模型在均值插补,回归插补,众数插补三种插补条件下的插补效果.随机模拟和实例分...     

基于偏正态数据下联合位置与尺度混合专家回归模型的参数估计北大核心CSCD

混合专家回归模型广泛应用于异质总体数据的分类,聚类及回归分析中.研究基于偏正态数据,提出了联合位置与尺度混合专家回归模型,该模型同时对位置,尺度和混合比例参数建模,应用MM算法和EM算法研究了该模型参数的极大似然估计.通过随机模拟和实例分析说明了该模型和方法的有效性与实用性.     

基于偏Laplace正态数据下位置、均值回归模型的参数估计

参数估计是一种基本的统计推断形式,也是统计学的一个重要分支.在分析偏态数据时,我们比较关注数据的众数、中位数和均值,但是偏Laplace正态数据的众数和中位数难以精确求出,因此用位置参数来近似代替.故本文提出偏Laplace正态数据下位置和均值回归模型,并研究该模型的参数估计,模拟和实例研究结果表明本文提出的模型和方法是科学合理的.     

众数自适应Lasso回归的统计推断

本文给出了自适应Lasso的众数回归模型,用来对众数回归模型的变量进行选择.对比传统的均值回归模型和中位数回归模型,众数回归在解决重尾、多峰分布问题时更加稳健.众数回归模型的主要估计方法是核估计方法,当自变量的数目较大时,该方法会产生难以忽略的计算误差.本文在核估计方法的众数回归模型基础上添加惩罚项,并通过自适应Lasso方法进行参数估计,有效的剔除了贡献率低的自变量,同时提高了计算的准确性.本文详细阐述了该计算方法,并在一些正则条件下,给出了模型的参数的估计方法和估计值的渐近正态性.模拟实验和实证分析研究了所提方法在有限样本下的性质.对比均值回归模型和传统的众数回归模型,添加自适应Lasso惩罚项的众数回归模型极大地提高了参数估计的准确性.     

具有高斯过程误差的函数型线性模型的统计诊断

提出了具有高斯过程误差的函数型回归模型的几种诊断方法.在此模型中,首先,在样条基的基础上,推导了回归系数函数的估计.随后,证明了数据删失模型和均值漂移模型的等价性.然后,研究了三种诊断方法,即残差分析、Cook距离和似然距离来诊断异常和强影响数据.最后,通过一个模拟例子和一个实例来阐述方法的有效性.     

Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies demonstrate the robustness of this flexible class against outlying and influential observations, as well as nice asymptotic properties of the proposed EM-type ML estimates. Finally, the methodology is illustrated using an ultrasonic calibration data.     

Robust errors-in-variables linear regression via Laplace distribution

Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace distribution. The finite sample performance of the proposed procedures is evaluated by simulation studies.     

On statistical models for regression diagnostics

In regression diagnostics, the case deletion model (CDM) and the mean shift outlier model (MSOM) are commonly used in practice. In this paper we show that the estimates of CDM and MSOM are equal in a wide class of statistical models, which include LSE, MLE, Bayesian estimate andM-estimate in linear and nonlinear regression models; MLE in generalized linear models and exponential family nonlinear models; MLEs of transformation parameters of explanatory variables in a Box-Cox regression models and so on. Furthermore, we study some models, in which, the estimates are not exactly equal but are approximately equal for CDM and MSOM.     

污染数据半参数回归模型中估计的相合性

利用最小二乘估计方法和权函数法给出了半参数模型Y=βX g(T) ε在某种污染方式下,,βg和污染系数的估计,并在适当条件下证明了它们具有相合性.     

删失截断情形下失效率变点模型的Bayes参数估计

通过添加部分缺失寿命变量数据,得到了删失截断情形下失效率变点模型相对简单的似然函数.讨论了所添加缺失数据变量的概率分布和随机抽样方法.利用Monte Carlo EM算法对未知参数进行了迭代.结合Metropolis-Hastings算法对参数的满条件分布进行了Gibbs抽样,基于Gibbs样本对参数进行估计,详细介绍了MCMC方法的实施步骤.随机模拟试验的结果表明各参数Bayes估计的精度较高.     

Asymptotic properties for the semiparametric regression model with randomly censored data

Suppose that the patients’ survival times.Y, are random variables following the semiparametric regression modelY = Xβ +g(T) + ε, where (X,T) is a radom vector taking values inR×[0,1],βis an unknown parameter,g (*) is an unknown smooth regression function andE is the random error with zero mean and variance σ². It is assumed that (X,T) is independent of E. The estimators andg _n (*) of P andg(*) are defined, respectively, when the observations are randomly censored on the right and the censoring distribution is unknown. Moreover, it is shown that is asymptotically normal andg _n (*) is weak consistence with rateO _p(n-1/3). Project supported by China Postdoctoral Science Foundation and the National Natural Science Foundation of China.     

Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data

The Birnbaum‐Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of the BS distribution, quantile regression modeling has not been considered for this distribution. To fill this gap, we introduce a class of quantile regression models based on the BS distribution, which allows us to describe positive and asymmetric data when a quantile must be predicted using covariates. We use an approach based on a quantile parameterization to generate the model, permitting us to consider a similar framework to generalized linear models, providing wide flexibility. The methodology proposed includes a thorough study of theoretical properties and practical issues, such as maximum likelihood parameter estimation and diagnostic analytics based on local influence and residuals. The performance of the residuals is evaluated by simulations, whereas an illustrative example of income data is conducted using the methodology to show its potential for applications. The numerical results report an adequate performance of the approach to quantile regression, indicating that the BS distribution is a good modeling choice when dealing with data that have both positive support and asymmetry. The economic implications of our investigation are discussed in the final section. Hence, it can be a valuable addition to the tool kit of applied statisticians and econometricians.     

On regression model selection for the data with correlated errors

A class of regression model selection criteria for the data with correlated errors is proposed. The proposed class of selection criteria is an estimator of weighted prediction risk. In addition, the proposed selection criteria are the generalizations of several commonly used criteria in statistical analysis. The theoretical and asymptotic properties for the class of criteria are established. Further, in the medium-sample case, the results based on a simulation study are quite consistent with the theoretical ones. The proposed criteria perform well in the simulations. Several applications are also given for a variety of statistical models.