Estimator of a change point in single index models

This paper considers the problem of change point in single index models. In order to obtain asymptotically valid confidence intervals for the estimation of the change point, the convergence rate and asymptotic distribution of the change point estimate is studied. Some simulation results are presented which show that the numerical performance of our estimator is satisfactory.     

斜率变点估计的强收敛速度

对至多只有一个斜率变点的模型,在误差分布为非正态时,本文利用滑窗方法给出了变点估计的强、弱相合性和强、弱收敛速度,同对在局部对立假设下研究了变点估计的O_p收敛速度.     

Nonparametric estimation for contamination distribution

In the paper, for the contamination distribution model F（x） = （1-α）F1（x）＋αF2（x）, the estimates of α and F1 （x） are studied using two different ways when F2 （x） is known and the strong consistency of the two estimates is proved. At the same time the consistency rate of estimate α is also given.     

Likelihood ratio test for a piecewise continuous Weibull model with an unknown change point

In this paper, we consider the likelihood ratio test for the scale and shape parameters in a piecewise continuous Weibull model with an unknown change point. Under the null hypothesis of no change in scale and shape parameters, we derive that the likelihood ratio process converges weakly to the squared Euclidian norm of a weighted mean zero Gaussian vector process.     

混合误差半参数回归模型估计的相合性

研究了误差为ψ混合和ψ混合序列的半参数回归模型,综合最小二乘法和非参数权函数估计方法,分别定义了待估参数β和未知函数夕的估计量βm,n和9m,n(χ).利用混合序列的矩不等式及凸函数的性质,在较弱的条件下证明了这些估计量的强相合性与矩相合性,这些结果推广了已有的相应的研究结果.     

Modified estimators for the change point in hazard function

We propose the consistent estimators for the change point in hazard function by improving the estimators in [A.P. Basu, J.K. Ghosh, S.N. Joshi, On estimating change point in a failure rate, in: S.S. Gupta, J.O. Berger (Eds.), Statistical Decision Theory and Related Topics IV, vol. 2, Springer-Verlag, 1988, pp. 239–252] and [H.T. Nguyen, G.S. Rogers, E.A. Walker, Estimation in change point hazard rate model, Biometrika 71 (1984) 299–304]. By a simulation study, we show that the proposed estimators are more efficient than the original estimators in many cases.     

至多一个变点的$\Gamma$分布的统计推断及在金融中的应用

对至多一个变点的Γ分布,即X1,X2…,Xn为一列相互独立的随机变量序列,且X1,X2,…,X[nΥ0]i．i．d～Γ(x;ν1,λ1),X[nΥ0] 1,X[nΥ0] 2,…,Xn i．i．d～Γ(x;ν2,λ2),其中Υ0未知,称Υ0为该序列的变点．在利用第一型极值分布逼近文中提出统计量的分布的基础上,给出了变点Υ0估计(?)的相合性及强弱收敛速度．最后给出了在金融序列上的应用．     

Consistency of kernel density estimators for causal processes

Using the blocking techniques and m-dependent methods,the asymptotic behavior of kernel density estimators for a class of stationary processes,which includes some nonlinear time series models,is investigated.First,the pointwise and uniformly weak convergence rates of the deviation of kernel density estimator with respect to its mean(and the true density function)are derived.Secondly,the corresponding strong convergence rates are investigated.It is showed,under mild conditions on the kernel functions and bandwidths,that the optimal rates for the i.i.d.density models are also optimal for these processes.     

小波检测并估计非参函数变点

研究随机设计下非参函数变点的小波检测与估计问题.将小波方法与设计点转化方法相结合给出变点的检测统计量并研究检测的一致性.给出了变点个数和变点位置的估计量,证明了变点个数估计量的相合性并得到变点位置估计量的收敛速度.     

A Fast Estimation Method for Mean Change Point in Massive Data Sets

When the sample size is $N$, the computational complexity of the least squares estimate of mean change point is O(N^2), and it's necessary to reduce the computational complexity in the case of huge data. In this paper, a two-stage fast scanning algorithm is proposed for the estimation of mean change point, and it is proved that this method has the same convergence speed and limiting distribution as the least squares estimation of mean change point, and the optimal complexity of the new algorithm is O(N^{4/3}\cdot b_n^{2/3}). We have conducted sufficient data experiments in terms of computation time and estimated efficiency, and the results show that the estimated efficiency of the new and old methods is similar, but the computation time of our method is obviously shortened.     

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)     

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.     

$\alpha$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.     

球面数据核近邻估计的强相合性

该文绘出了球面数据密度函数的核近邻估计,通过对核估计与近邻估计相互关系的讨论,建立了核近邻估计的逐点强相合性及一致强相合性．     

On Consistency of the Nearest Neighbor Estimator of the Density Function and Its Applications

In this paper, we mainly study the consistency of the nearest neighbor estimator of the density function based on asymptotically almost negatively associated samples. The weak consistency,strong consistency, uniformly strong consistency and the convergence rates are established under some mild conditions. As applications, we further investigate the strong consistency and the rate of strong consistency for hazard rate function estimator.     

随机删失下半参数估计的强相合性

主要讨论了随机删失下的部分线性模型,利用基于分布函数的核估计和最小二乘法,给出了删失情况下参数和非参数部分的估计,并证明了它们的强相合性.     

Minimum-norm fixed point of nonexpansive mappings with applications

The purpose of this paper is to present two new iterative algorithms to find the minimum norm fixed point of nonexpansive nonself-mappings in the framework of Hilbert spaces. The results presented in this paper improve and extend the corresponding ones announced by Yao and Xu [Yao Y, Xu HK. Iterative methods for finding minimum norm fixed point of mappings with applications. Optimization. 2011;60:645–658] and many others. Some applications to convex minimization and split feast problems(SFP) are also included.     

Proximal point algorithm for infinite pseudo-monotone bifunctions

In this paper, first we study the weak convergence of the proximal point algorithm for an infinite family of equilibrium problems of pseudo-monotone type in Hilbert spaces. Then with additional conditions on the bifunctions, we prove the strong convergence for the family to a common equilibrium point. We also study a regularization of Halpern type and prove the strong convergence of the generated sequence to an equilibrium point of the family of infinite pseudo-monotone bifunctions without any additional assumptions on the bifunctions. A concrete example of a family of pseudo-monotone bifunctions is also presented.     

On stable laws for estimating functions and derived estimators

Summary Stable laws forM-estimators, maximum likelihood and other estimators and obtained through parallel results for the estimating functions and relative compactness of some related estimating functional processes. Work supported by the Office of Naval Research, Contract No. N00014-83-K-0387.