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。     

半参数单调回归EV模型的估计

半参数回归模型在工程、经济和医学问题中有着广泛的应用,但是实际问题中数据往往带有测量误差,并且许多情况下解释变量和某些响应变量之间的关系是单调的,此时普通的参数最小二乘估计是有偏的,且非参数部分的估计无法保证单调性.本文针对这两个问题研究了半参数单调回归模型,得到了参数的无偏估计,同时非参数部分估计具有单调性,并得到了...     

污染数据半参数回归模型的估计方法

考虑半参数回归模型ｙｊ＝ｘｊβ＋ｇ（ｔｊ）＋εｊ，ｊ＝１，２，…，ｎ其中（ｘｊ，ｔｊ）为取值于Ｒ×［０，１］上的固定设计，β为未知参数，ｇ是定义在［０，１］上的未知函数，εｊ为随机误差，Ｅεｊ＝０，Ｅε２ｊ＝σ２１．但ｙ１，…，ｙｎ受到另一独立同分布的随机变量序列ｕ１，…，ｕｎ两种不同方式的污染，ｕｊ与ｙｊ独立．本文利用矩估计方法给出两种污染方式下β、ｇ及污染参数的估计．     

相依误差下半参数回归模型的估计

本文在固定设计及相依误差下获得了半参数回归模型的参数估计的强相合性．     

基于多元半参数回归建模的机械加工过程误差分析

机械加工过程的误差分析对于消减误差源、提升工序质量具有重要的实际指导意义．研究者们尝试了多种误差分析方法用于获得误差源对加工质量的作用规律,然而由于加工误差的复杂性,各有局限性．本文则根据工程经验和数学推导建立了用于一般机械加工过程误差分析的多元半参数回归模型,基于测量数据详细讨论了所建立模型的参数估计和非参数规律辨识问题．仿真实例证明,与现有方法相比,本文所提方法能够准确估计上游工序传递误差,有效辨识当前工序系统误差的作用规律,研究成果为一般机械加工过程的误差分析奠定了基础．     

误差为线性过程时非参数回归模型变点的两步估计

本文给出误差为线性过程时非参数回归模型变点两步估计.第一步,给出变点位置的初始估计,并且证明了该估计量的相合性;第二步,在变点初始估计值的基础上推导出变点位置的最终估计量并给出了该估计量的收敛速度.数值模拟以及尼罗河数据实例分析的结果说明方法的有效性.     

纵向数据半参数回归模型估计的强相合性

本文考虑如下纵向数据半参数回归模型:yij=x'ijβ g(tij) eij。基于最小二乘法和一般的非参数权函数方法给出了模型中参数β,回归函数g(·)和误差方差σ2的估计,并在适当条件下证明了估计量的强相合性。     

截尾数据下一类半参数回归模型的估计

讨论了一类半参数回归模型y =x′β+g(t′α) +e .假定y被随机变量T右侧截尾 ,T与y独立 ,T～G。在G已知和未知两种情况下 ,构造了α、β和g(·) 的强相合估计     

约束条件下的线性贝叶斯估计

本文提出等式约束下线性模型中回归参数的线性贝叶斯估计,证明其在均方误差矩阵准则下相对于约束最小二乘估计的优越性,并采用蒙特卡洛模拟和数值算例验证其优越性．     

半参数回归模型的泛补偿最小二乘估计

本文首先提出泛补偿最小二乘法：接着,使用该法考虑半参数回归模型,得到了参数及非参数的估计。然后,将泛补偿最小二乘法与补偿最小二乘法进行了比较;最后用模拟的算例说明了该方法的有效性。     

直线回归的最小面积法

自变量、因变量的不同选择,用传统的最小二乘法得到的回归方程是不同的。提出直线回归的最小面积法解决这一问题,推导了该方法的计算公式并讨论了该方法的一些性质。     

Partitioned Ridge Regression

In this paper we examine the relationship between the general ridge estimator and the standardized ridge estimator.     

Local Influence of Predictors in Multiple Linear Regression

Local influence diagnostics can be used to assess the influence of predictor values in multiple linear regression. For n observations and k regressors, an eigenanalysis of an nk ×nk matrix is required to assess the influence on the estimated coefficients. We provide the analytic expressions for the eigenvectors and show that they are easily computed, describe influence on the parameter estimates of a principal components regression, and are related to leverage, outliers, and added-variables plots. The results indicate that multicollinearity and overfitting contribute to a fitted model's sensitivity, leading to strategies for model assessment and selection.     

噪声为厚尾过程的非参数函数变点的小波估计

本文给出了随机设计下非参数回归模型中噪声为无穷方差过程的小波检测和估计方法。利用基于经验小波系数的检验统计量,在原假设成立的条件下,推导出任意尺度上检验的临界值,证明了检验的一致性;在备择假设成立的条件下,得到变点个数、变点位置的相合估计与收敛速度。数值模拟以及IBM股票数据实例分析的结果均表明方法是有效的。     

Phase I control chart based on a kernel estimator of the quantile function

To measure the statistical performance of a control chart in Phase I applications, the in‐control average run length (ARL) is the most frequently used parameter. In typical start up situations, control limits must be computed without knowledge of the underlying distribution of the quality characteristic. Assumptions of an underlying normal distribution can increase the probability of false alarms when the underlying distribution is non‐normal, which can lead to unnecessary process adjustments. In this paper, a control chart based on a kernel estimator of the quantile function is proposed. Monte Carlo simulation was used to evaluate the in‐control ARL performance of this chart relative to that of the Shewhart individuals control chart. The results indicate that the proposed chart is more robust to deviations in the assumed underlying distribution (with respect to the in‐control ARL) and results in an alternative method of designing control charts for individual units. Copyright © 2011 John Wiley & Sons, Ltd.     

Simultaneous Envelopes for Multivariate Linear Regression

We introduce envelopes for simultaneously reducing the predictors and the responses in multivariate linear regression, so the regression then depends only on estimated linear combinations of X and Y. We use a likelihood-based objective function for estimating envelopes and then propose algorithms for estimation of a simultaneous envelope as well as for basic Grassmann manifold optimization. The asymptotic properties of the resulting estimator are studied under normality and extended to general distributions. We also investigate likelihood ratio tests and information criteria for determining the simultaneous envelope dimensions. Simulation studies and real data examples show substantial gain over the classical methods, like partial least squares, canonical correlation analysis, and reduced-rank regression. This article has supplementary material available online.     

Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression

I extend the concept of partial least squares (PLS) into the framework of generalized linear models. A spectroscopy example in a logistic regression framework illustrates the developments. These models form a sequence of rank 1 approximations useful for predicting the response variable when the explanatory information is severely ill-conditioned. Iteratively reweighted PLS algorithms are presented with various theoretical properties. Connections to principal-component and maximum likelihood estimation are made, as well as suggestions for rules to choose the proper rank of the final model.     

自变量含有类型变量的线性回归模型的树方法

给出了求解自变量含有类型变量的线性回归模型的树方法。它是一个非参数方法。讨论了修剪树对参数估计和预测的影响,给出了通过修剪树提高参数估计和预测精度的充要条件。