Étude asymptotique locale de l'estimateur par ondelettesA law of the iterated logarithm for wavelet density estimator

We state a pointwise central limit theorem for the linear wavelet density estimator in a more general setting than the result of Wu [12]. Furthermore, we also give a pointwise law of the iterated logarithm for this density estimator. Our proof of the law of the iterated logarithm uses the results of Mason [9] on the asymptotic behavior of the tail empirical process. To cite this article: A. Massiani, C. R. Acad. Sci. Paris, Ser. I 335 (2002) 553–556.     

Vitesse de convergence uniforme presque sûre de l'estimateur linéaire par méthode d'ondelettes

We establish an uniform law of the iterated logarithm for the linear wavelet density estimator. A key tool in the proof of this result is the functional law of the iterated logarithm for the increments of the empirical process proved by Deheuvels and Mason (Ann. Probab. 20 (1992) 1248–1287). To cite this article: A. Massiani, C. R. Acad. Sci. Paris, Ser. I 337 (2003).     

随机左截断数据下乘积限估计的强逼近及其应用

本文建立了左截断数据下乘积限估计的强表示结果,其误差项的收敛速度达到重对数律。作为应用,推出了乘积限估计的重对数律和强逼近等深刻结果。     

Statistiques d'une saisonnalité perturbée par un processus a représentation autorégressiveStatistics of seasonality perturbed by time continuous processes with autoregressive representation

We consider limit theorems for an estimator of a seasonality when it is perturbed by a time continuous process admitting a Banach autoregressive representation. From the compact iterated logarithm law we derive confidence regions for a(·) in the Banach space of continuous functions. When a(·) belongs to a finite dimensional subspace, we study the estimation of a(·) by projection and we estimate the dimension when it is unknown. To cite this article: T. Mourid, C. R. Acad. Sci. Paris, Ser. I 334 (2002) 909–912.     

分位点函数的光滑非参数估计的BAHADUR表示

文中对分位函数给出了具有更广泛应用的光滑分位估计,证明了该光滑分位估计的逐点和一致的Bahadur强表示定理;并由此结果推导了估计的重对数律,强逼近等深刻结果。     

On the minimal penalty for Markov order estimation

We show that large-scale typicality of Markov sample paths implies that the likelihood ratio statistic satisfies a law of iterated logarithm uniformly to the same scale. As a consequence, the penalized likelihood Markov order estimator is strongly consistent for penalties growing as slowly as log log n when an upper bound is imposed on the order which may grow as rapidly as log n. Our method of proof, using techniques from empirical process theory, does not rely on the explicit expression for the maximum likelihood estimator in the Markov case and could therefore be applicable in other settings.     

A law of the iterated logarithm forL 1-norm kernel estimator of the conditional median

Let (X, Y) be a pair of two-dimensional random variables. In this paper we establish a law of the iterated logarithm for theL ₁-norm kernel estimator of the conditional median function ofY onX, which gives sharp pointwise rate of strong consistency.This project is supported by the National Natural Science Foundation of China under Contract 18901001.     

Laws of the iterated logarithm and an almost sure invariance principle for mixing <Emphasis Type="Italic">B</Emphasis>-valued random variables and autoregressive processes

Under optimal moment conditions, we prove the compact law of the iterated logarithm and the almost sure invariance principle for ψ-mixing random variables with values in type 2 Banach spaces. These results, together with the bounded law of the iterated logarithm proved earlier by author, allow us to prove the same kind of results for the Banach space valued autoregressive processes with ψ-mixing innovations. The results for autoregressive processes can be considered as asymptotic properties of the estimator of mean.     

删失数据平滑非参数分位估计

文中在随机右删失意义下，对于未知分布函数的分位点，基于ＰＬ估计给出了一种平滑的非参数核分位估计，推导出了该估计的逐点和一致强弱Ｂａｈａｄｕｒ类型表示定理，并由此结果获得了平滑分位计的渐近正态性及重对数律等深刻结果。     

Asymptotics for a censored generalized linear model with unknown link function

For censored response variable against projected co-variable, a generalized linear model with an unknown link function can cover almost all existing models under censorship. Its special cases include the accelerated failure time model with censored data. Such a model in the uncensored case is called the single-index model in econometrics. In this paper, we systematically study the asymptotic properties. We derive the central limit theorem and the law of the iterated logarithm for an estimator of the direction parameter. We also obtain the optimal convergence rate of an estimator of the unknown link function in the model.      

Asymptotic behaviours for the trajectory fitting estimator in Ornstein–Uhlenbeck process with linear drift

We study the asymptotic behaviours for the trajectory fitting estimator in the Ornstein–Uhlenbeck process with linear drift. Deviation inequality, moderate deviations, Berry–Esseen bound and the law of iterated logarithm (LIL) of this estimator can be obtained. Moreover, as an application of the Berry–Esseen bound, we can get the precise rate in LIL. The main method of this paper is the deviation inequality for multiple Wiener-Itô integrals [P. Major, On a multivariate version of Bernsteins inequality. Electron. J. Prob. 12 (2007), pp. 966–988; P. Major, Tail behavior of multiple integrals and U-statistics. Probab. Surv. 2 (2005), pp. 448–505].     

Vitesse de convergence presque sûre de l'estimateur à noyau du maximum de vraisemblance local

We consider the local maximum likelihood estimation of $\theta (x)$, unknown parameter of the conditional distribution of Y given $X=x$. The aim of this Note is the study of strong uniform consistency rates of the local maximum likelihood kernel estimator. Under suitable regularity conditions, we establish a uniform law of the logarithm for the maximal deviation of this estimator. The method of proof is based upon functional limit laws derived by modern empirical process theory. To cite this article: D. Blondin, C. R. Acad. Sci. Paris, Ser. I 342 (2006).     

Estimation of a quantile in some nonstandard cases

A necessary condition for the asymptotic normality of the sample quantile estimator isf(Q(p))=F(Q(p))>0, whereQ(p) is thep-th quantile of the distribution functionF(x). In this paper, we estimate a quantile by a kernel quantile estimator when this condition is violated. We have shown that the kernel quantile estimator is asymptotically normal in some nonstandard cases. The optimal convergence rate of the mean squared error for the kernel estimator is obtained with respect to the asymptotically optimal bandwidth. A law of the iterated logarithm is also established.This research was partially supported by the new faculty award from the University of Oregon.     

非参数回归函数的随机窗宽核估计的重对数律

设(X,Y),(X_1,Y_1),(X_2,Y_2),…为 i.i.d.二维随机变量序列,具有联合分布F(x,y)及密度 f(x,y).X 的边际分布和密度分别记为 F_X(x)和 f_X(x).记 m(x)=E{Y|X=x)}为 Y 对 X 的回归函数.为估计 m(x),Nadaraya 和 watson 独立地引进了如下形式的核估计     

The effects on convergence of substituting parameter estimates into U-statistics and other families of statistics

Summary Substituting an estimator in a statistic will often affect its limiting distribution. Sukhatme (1958), Randles (1982), and Pierce (1982) all consider the changes, if any, in the statistic's limiting normal distribution. This paper gives conditions for a law of the iterated logarithm for U-statistics which have a kernel with an estimator substituted into it. It also gives conditions for both strong and weak convergence. Applications of the theory are illustrated by constructing a sequential test for scale differences with power one. The theory also produces convergence results for adaptive M-estimators and for cross-validation assessment statistics. In addition, it is shown how to extend LIL results to a broad class of statistics with estimators substituted into them by use of the differential. In particular, a law of the iterated logarithm is described for adaptive L-statistics and is illustrated by an example of de Wet and van Wyk (1979).     

左截断右删失数据下半参数模型风险率函数估计

文章给出了右删失左截断数据半参数模型下的风险率函数估计,讨论了风险率函数估计的渐近性质,获得了这些估计的渐近正态性,对数律和重对数律.由于假定删失机制服从半参数模型下,从而知道模型的更多信息,因此对于给出参数的极大似然估计,可以改进风险率函数估计的渐近性质.也就是说,删失数据模型具有半参数的辅助信息下, 风险率函数估计的渐近方差比通常的完全非参数的估计的渐近方差更小.这说明加入了额外的信息提高了风险率函数估计的效率.     

How big are the increments of G-Brownian motion?

In this paper,we investigate the problem:How big are the increments of G-Brownian motion.We obtain the Csrg and R′ev′esz’s type theorem for the increments of G-Brownian motion.As applications of this result,we get the law of iterated logarithm and the Erds and R′enyi law of large numbers for G-Brownian motion.Furthermore,it turns out that our theorems are natural extensions of the classical results obtained by Csrg and R′ev′esz(1979).     

Error density estimation in high-dimensional sparse linear model

This paper is concerned with the error density estimation in high-dimensional sparse linear model, where the number of variables may be larger than the sample size. An improved two-stage refitted cross-validation procedure by random splitting technique is used to obtain the residuals of the model, and then traditional kernel density method is applied to estimate the error density. Under suitable sparse conditions, the large sample properties of the estimator including the consistency and asymptotic normality, as well as the law of the iterated logarithm are obtained. Especially, we gave the relationship between the sparsity and the convergence rate of the kernel density estimator. The simulation results show that our error density estimator has a good performance. A real data example is presented to illustrate our methods.

     

On Least Squares Estimation for Stable Nonlinear AR Processes

Following a Markov chain approach, this paper establishes asymptotic properties of the least squares estimator in nonlinear autoregressive (NAR) models. Based on conditions ensuring the stability of the model and allowing the use of a strong law of large number for a wide class of functions, our approach improves some known results on strong consistency and asymptotic normality of the estimator. The exact convergence rate is established by a law of the iterated logarithm. Based on this law and a generalized Akaike's information criterion, we build a strongly consistent procedure for selection of NAR models. Detailed results are given for familiar nonlinear AR models like exponential AR models, threshold models or multilayer feedforward perceptions.     

A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data

In this paper we consider the TJW product-limit estimatorF_n(x) of an unknown distribution functionFwhen the data are subject to random left truncation and right censorship. An almost sure representation of PL-estimatorF_n(x) is derived with an improved error bound under some weaker assumptions. We obtain the strong approximation ofF_n(x)−F(x) by Gaussian processes and the functional law of the iterated logarithm is proved for maximal derivation of the product-limit estimator toF. A sharp rate of convergence theorem concerning the smoothed TJW product-limit estimator is obtained. Asymptotic properties of kernel estimators of density function based on TJW product-limit estimator is given.