Nonparametric conditional hazard rate estimation: A local linear approach

Spierdijk, Laura

doi:10.1016/j.csda.2007.08.007

Cited by 49 publications

(40 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this shouldn't cause much concern, as firstly, the result proved in Gonzalez-Manteigaa and Cadarso-Suarez (1994) on the local K-M estimator (6) doesn't rely on the positivity of B nk (.). Secondly, in practice, a simple truncation can always be applied to ensure 0 ≤Ĝ n (.|x) ≤ 1; see Spierdijk (2008) for a similar observation.…”

Section: The Model and Estimation Methodsmentioning

confidence: 99%

Global Bahadur representation for nonparametric censored regression quantiles and its applications

Kong

Linton

Xia

2011

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract. This paper is concerned with the nonparametric estimation of regression quantiles where the response variable is randomly censored. Using results on the strong uniform convergence of U-processes, we derive a global Bahadur representation for the weighted local polynomial estimators, which is sufficiently accurate for many further theoretical analyses including inference. Terms of use: Documents in EconStor mayWe consider two applications in detail: estimation of the average derivative, and estimation of the component functions in additive quantile regression models.

show abstract

Section: The Model and Estimation Methodsmentioning

confidence: 99%

Global Bahadur representation for nonparametric censored regression quantiles and its applications

Kong

Linton

Xia

2011

View full text Add to dashboard Cite

show abstract

“…We consider four two-dimensional hazards. The first two were also considered in Nielsen (1998) and the other two were also considered in Spierdijk (2008). The assumed true hazard rates are:…”

Section: Simulation Settingsmentioning

confidence: 99%

“…The objectives we pursue through simulation experiments are twofold: (1) to compare the local linear (LL) estimator with the local linear and local constant (LLLC) estimator that generalises the estimator by Spierdijk (2008), and (2) to evaluate the performance of the two bandwidth selectors described in Section 3, namely the cross-validation bandwidth b CV and the do-validation bandwidth b DO . To achieve the first objective we provide a numerical comparison in two scenarios.…”

Section: The Objectivesmentioning

confidence: 99%

Bandwidth selection in marker dependent kernel hazard estimation

Pérez

Janys

Miranda

et al. 2013

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractPractical estimation procedures for local linear estimation of an unrestricted failure rate when more information is available than just time are developed. This extra information could be a covariate and this covariate could be a time series. Time dependent covariates are sometimes called markers, and failure rates are sometimes called hazards, intensities or mortalities. It is shown through simulations and a practical example that the fully local linear estimation procedure exhibits an excellent practical performance. Two different bandwidth selection procedures are developed. One is an adaptation of classical cross-validation, and the other one is indirect cross-validation. The simulation study concludes that classical cross-validation works well on continuous data while indirect cross-validation performs only marginally better. However, cross-validation breaks down in the practical data application to old-age mortality. Indirect cross-validation is thus shown to be superior when selecting a fully feasible estimation method for marker dependent hazard estimation.

show abstract

“…Many references, practical examples and simulations in the case of non parametric estimation using local linear approximations can be found in Spierdijk [28]. The main aim of this paper, is to study, under general conditions, the asymptotic proprieties of the functional data kernel estimate of the conditional hazard function introduced by Ferraty et al [12].…”

Section: Introductionmentioning

confidence: 99%

Conditional risk estimate for functional data under strong mixing conditions

Rabhi¹,

Soltani²,

Traore³

2015

JSTA

View full text Add to dashboard Cite

We consider the problem of nonparametric estimation of the conditional hazard function for functional mixing data. More precisely, given a strictly stationary random variables Z i = (X i , Y i ) i∈N , we investigate a kernel estimate of the conditional hazard function of univariate response variable Y i given the functional variable X i . The principal aim of this paper is to give the mean squared convergence rate and to prove the asymptotic normality of the proposed estimator.

show abstract

Nonparametric conditional hazard rate estimation: A local linear approach

Cited by 49 publications

References 44 publications

Global Bahadur representation for nonparametric censored regression quantiles and its applications

Global Bahadur representation for nonparametric censored regression quantiles and its applications

Bandwidth selection in marker dependent kernel hazard estimation

Conditional risk estimate for functional data under strong mixing conditions

Contact Info

Product

Resources

About