PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA

The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.     

Consistency of change point estimators for symmetrical stable distribution with parameters shift

Assume that the characteristic indexαof stable distribution satisfies 1<α<2,and that the distribution is symmetrical about its mean.We consider the change point estimators for stable distribution withαor scale parameterβshift.For the one case that mean is a known constant,ifαorβchanges,then density function will change too.To this end,we suppose the kernel estimation for a change point.For the other case that mean is an unknown constant,we suppose to apply empirical characteristic function to estimate the change-point location.In the two cases,we consider the consistency and strong convergence rate of estimators.Furthermore,we consider the mean shift case.If mean changes,then corresponding characteristic function will change too.To this end,we also apply empirical characteristic function to estimate change point.We obtain the similar convergence rate.Finally,we consider its application on the detection of mean shift in financial market.     

Nonparametric estimation of a regression function by delta sequences

A somewhat more general class of nonparametric estimators for estimating an unknown regression functiong from noisy data is proposed. The regressor is assumed to be defined on the closed interval [0, 1]. This class of estimators is shown to be pointwisely consistent in the mean square sense and with probability one. Further, it turns out that these estimators can be applied to a wide class of noises.     

稳定样本过程的非参数小波估计

本文中我们采用小波方法估计了一列基于Ф混合平稳过程的v-稳定样本的公共密度函数。     

Asymptotic Properties of Nonparametric Kernel Regression Estimator for \rho-Mixing Samples

For \rho-mixing samples, we discuss thestrong consistency of the nonparametric kernel regression estimator proposed by Gasser and Muller. Under more weaker conditions, its strong consistency and uniformly strong consistency are proved.     

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??For \rho-mixing samples, we discuss thestrong consistency of the nonparametric kernel regression estimator proposed by Gasser and Muller. Under more weaker conditions, its strong consistency and uniformly strong consistency are proved.     

α混合下非参数回归函数改良分割估计的强相合性及收敛速度

In this paper, we study the strong consistency and convergence rate of modified partitioning estimate of nonparametric regression function under the sample {(Xi, Yi),i ≥ 1} that is α sequence taking values in Rd × R1 with identical distribution.     

NA样本非参数回归权函数估计的强相合性

在 NA样本下 ,讨论了非参数回归模型中权函数估计的强相合性及强一致相合性 ,并把这个结果应用于 Gasser- Muller估计和 Priestley and Chao估计 .     

污染线性模型的非参数估计

Abstract. In this paper, the following contaminated linear model is considered:     

半参数方差误差模型的估计

本文综合近邻权函数法及最小二乘法，用两阶段最小二乘估计的方法得到了半参数EV模型中参数的估计量及其强相合性，渐近正态性。同时也得到了非参数函数的估计量及其强相合性，一致强相合性。     

面板数据公共变点的非参数最大似然估计

Gao Pengli;Xia Zhiming(School of Mathematics,Northwest University,Xi'an 710127,China)     

多元非参数计量经济模型的变窗宽局部线性估计

在随机设计(模型中所有变量为随机变量)下,提出了非参数计量经济模型的变窗宽局部线性估计,并利用概率论中大数定理和中心极限定理,在内点处证明了它的一致性和渐近正态性.它在内点处的收敛速度达到了非参数函数估计的最优收敛速度.     

Nonparametric density estimation for linear processes with infinite variance

We consider nonparametric estimation of marginal density functions of linear processes by using kernel density estimators. We assume that the innovation processes are i.i.d. and have infinite-variance. We present the asymptotic distributions of the kernel density estimators with the order of bandwidths fixed as h =  cn ^−1/5, where n is the sample size. The asymptotic distributions depend on both the coefficients of linear processes and the tail behavior of the innovations. In some cases, the kernel estimators have the same asymptotic distributions as for i.i.d. observations. In other cases, the normalized kernel density estimators converge in distribution to stable distributions. A simulation study is also carried out to examine small sample properties.     

$\alpha$混合下非参数回归函数改良分割估计的强相合性及收敛速度

本文研究了在样本$(X_1,Y_1),(X_2,Y_2),\ldots,(X_n,Y_n)$ 为取值于$R^{d}\times R^{1}$的同分布的$\alpha$混合序列时,回归函数改良分割估计的强相合性和收敛速度.     

线性过程误差下的非参数回归模型的注记

对于非参数回归模型Yni=g(xni)+εni,1in,用一般非参数方法,定义了未知函数g(.)的估计量gn(x),当误差序列{εni,1in}为一弱平稳线性过程序列时,在一定条件下,获得了估计量gn(x)的一致强相合性.     

An estimation of distribution algorithm for nurse scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.     

Nonparametric Regression with Singular Design

Theories of nonparametric regression are usually based on the assumption that the design density exists. However, in some applications such as those involving high-dimensional or chaotic time series data, the design measure may be singular and may be likely to have a fractal (nonintegral) dimension. In this paper, the popular Nadaraya–Watson estimator is studied under the general setup that the continuity of the design measure is governed by the local or pointwise dimension. It will be shown in the iid setup that the nonparametric regression estimator achieves a convergence rate which is dependent only on the pointwise dimension. The case of time series data is also studied. For the latter case, a new mixing condition is introduced, and an assumption of marginal or joint density is completely avoided. Three examples, a fractal regression and two applications for predicting chaotic time series, are used to illustrate the implications of the obtained results.     

Nonparametric estimation of a conditional distribution from length-biased data

In this paper we consider the problem of estimating a conditional distribution function in a nonparametric way, when the response variable is nonnegative, and the observational procedure is length-biased. We propose a proper adaptation of the estimate to right-censoring provoked by limitation in following-up. Large sample analysis of the introduced estimator is given, including rates of convergence, limiting distribution, and efficiency results. We show that the length-bias model results in less variance in estimation, when compared to methods based on observed truncation times. Practical performance of the proposed estimator is explored through simulations. Application to unemployment data analysis is provided.     

Nonparametric estimation of the dependence function for a multivariate extreme value distribution

Understanding and modeling dependence structures for multivariate extreme values are of interest in a number of application areas. One of the well-known approaches is to investigate the Pickands dependence function. In the bivariate setting, there exist several estimators for estimating the Pickands dependence function which assume known marginal distributions [J. Pickands, Multivariate extreme value distributions, Bull. Internat. Statist. Inst., 49 (1981) 859-878; P. Deheuvels, On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions, Statist. Probab. Lett. 12 (1991) 429-439; P. Hall, N. Tajvidi, Distribution and dependence-function estimation for bivariate extreme-value distributions, Bernoulli 6 (2000) 835-844; P. Capéraà, A.-L. Fougères, C. Genest, A nonparametric estimation procedure for bivariate extreme value copulas, Biometrika 84 (1997) 567-577]. In this paper, we generalize the bivariate results to p-variate multivariate extreme value distributions with p?2. We demonstrate that the proposed estimators are consistent and asymptotically normal as well as have excellent small sample behavior.