Nonparametric inference for sequential <Emphasis Type="Italic">k</Emphasis>-out-of-<Emphasis Type="Italic">n</Emphasis> systems

The k-out-of-n model is commonly used in reliability theory. In this model the failure of any component of the system does not influence the components still at work. Sequential k-out-of-n systems have been introduced as an extension of k-out-of-n systems where the failure of some component of the system may influence the remaining ones. We consider nonparametric estimation of the cumulative hazard function, the reliability function and the quantile function of sequential k-out-of-n systems. Furthermore, nonparametric hypothesis testing for sequential k-out-of-n-systems is examined. We make use of counting processes to show strong consistency and weak convergence of the estimators and to derive the asymptotic distribution of the test statistics.     

Nonparametric density estimation for nonmixing approximable Stochastic Processes

The author deals with nonparametric density estimation for stochastic processes which satisfy the L _∞-approximability property. He considers a Parzen–Rosenblatt estimator of the density for general stationary L _∞-approximable processes. He states conditions under which it is consistent and investigates its rate of convergence. Finally, he applies his results to general nonmixing linear processes and nonmixing nonlinear autoregressive processes.     

Bandwidth choice for local polynomial estimation of smooth boundaries

Local polynomial methods hold considerable promise for boundary estimation, where they offer unmatched flexibility and adaptivity. Most rival techniques provide only a single order of approximation; local polynomial approaches allow any order desired. Their more conventional rivals, for example high-order kernel methods in the context of regression, do not have attractive versions in the case of boundary estimation. However, the adoption of local polynomial methods for boundary estimation is inhibited by lack of knowledge about their properties, in particular about the manner in which they are influenced by bandwidth; and by the absence of techniques for empirical bandwidth choice. In the present paper we detail the way in which bandwidth selection determines mean squared error of local polynomial boundary estimators, showing that it is substantially more complex than in regression settings. For example, asymptotic formulae for bias and variance contributions to mean squared error no longer decompose into monotone functions of bandwidth. Nevertheless, once these properties are understood, relatively simple empirical bandwidth selection methods can be developed. We suggest a new approach to both local and global bandwidth choice, and describe its properties.     

Nonparametric estimation of mixed partial derivatives of a multivariate density

On the basis of a random sample of size n on an m-dimensional random vector X, this note proposes a class of estimators f_n^(p) of f^(p), where f is a density of X w.r.t. a σ-finite measure dominated by the Lebesgue measure on R^m, p = (p₁,…,p_m), p_j ≥ 0, fixed integers, and for x = (x₁,…,x_m) in R^m, f^(p)(x) = ?^p₁+…+p_m f(x)/(?^p₁x₁ … ?^p_mx_m). Asymptotic unbiasedness as well as both almost sure and mean square consistencies of f_n^(p) are examined. Further, a necessary and sufficient condition for uniform asymptotic unbisedness or for uniform mean square consistency of f_n^(p) is given. Finally, applications of estimators of this note to certain statistical problems are pointed out.     

核估计量、窗宽与置信带

本文对非参数回归曲线提出一种新的核估计量和窗宽选择方法及其修正偏倚置信带 .仅利用该回归曲线的估计量和选择数据的窗宽构造这些置信带 .证明了在大样本的意义下 ,这种修正偏倚置信带和Bonferroni型带具有渐近修正范围概率的性质 .并且通过MonteCarlo实验研究了它在小样本中的性质 .在模拟研究中已经证明 ,这种修正偏倚置信带方法是很有效的 ,即使在样本容量n=1 0 0的情况下 ,它也接近给定的范围概率 .     

Model selection for regression on a fixed design

We deal with the problem of estimating some unknown regression function involved in a regression framework with deterministic design points. For this end, we consider some collection of finite dimensional linear spaces (models) and the least-squares estimator built on a data driven selected model among this collection. This data driven choice is performed via the minimization of some penalized model selection criterion that generalizes on Mallows' C _p . We provide non asymptotic risk bounds for the so-defined estimator from which we deduce adaptivity properties. Our results hold under mild moment conditions on the errors. The statement and the use of a new moment inequality for empirical processes is at the heart of the techniques involved in our approach. Received: 2 July 1997 / Revised version: 20 September 1999 / Published online: 6 July 2000     

Data Adaptive Ridging in Local Polynomial Regression

Abstract

When estimating a regression function or its derivatives, local polynomials are an attractive choice due to their flexibility and asymptotic performance. Seifert and Gasser proposed ridging of local polynomials to overcome problems with variance for random design while retaining their advantages. In this article we present a data-independent rule of thumb and a data-adaptive spatial choice of the ridge parameter in local linear regression. In a framework of penalized local least squares regression, the methods are generalized to higher order polynomials, to estimation of derivatives, and to multivariate designs. The main message is that ridging is a powerful tool for improving the performance of local polynomials. A rule of thumb offers drastic improvements; data-adaptive ridging brings further but modest gains in mean square error.     

Adaptive Free-Knot Splines

This article proposes a function estimation procedure using free-knot splines as well as an associated algorithm for implementation in nonparametric regression. In contrast to conventional splines with knots confined to distinct design points, the splines allow selection of knot numbers and replacement of knots at any location and repeated knots at the same location. This exibility leads to an adaptive spline estimator that adapts any function with inhomogeneous smoothness, including discontinuity, which substantially improves the representation power of splines. Due to uses of a large class of spline functions, knot selection becomes extremely important. The existing knot selection schemes—such as stepwise selection—suffer the difficulty of knot confounding and are unsuitable for our purpose. A new knot selection scheme is proposed using an evolutionary Monte Carlo algorithm and an adaptive model selection criterion. The evolutionary algorithm locates the optimal knots accurately, whereas the adaptive model selection strategy guards against the selection error in searching through a large candidate knot space. The performance of the procedure is examined and illustrated via simulations. The procedure provides a significant improvement in performance over the other competing adaptive methods proposed in the literature. Finally, usefulness of the procedure is illustrated by an application to actual dataset.     

Measuring the impacts of production risk on technical efficiency: A state-contingent conditional order-m approach

This article studies the influence of risk on farms’ technical efficiency levels. The analysis extends the order-m efficiency scores approach proposed by Daraio and Simar (2005) to the state-contingent framework. The empirical application focuses on cross section data of Catalan specialized crop farms from the year 2011. Results suggest that accounting for production risks increases the technical performance. A 10% increase in output risk will result in a 2.5% increase in average firm technical performance.     

On Convergence of Convex Minorant Algorithms for Distribution Estimation with Interval-Censored Data

Abstract

Local convergence results of the convex minorant (CM) algorithm to obtain the nonparametric maximum-likelihood estimator of a distribution under interval-censored observations are given. We also provide a variation of the CM algorithm, which yields global convergence. The algorithm is illustrated with data on AIDS survival time in 92 members of the U.S. Air Force.