References of "Van Keilegom, Ingrid"
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See detailLikelihood based inference for semi-competing risks
Heuchenne, Cédric ULg; Laurent, Stéphane ULg; Legrand, Catherine et al

in Communications in Statistics : Simulation & Computation (2014), 43(5), 1112-1132

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See detailEstimation of the error density in a semiparametric transformation model
Colling, Benjamin; Heuchenne, Cédric ULg; Samb, Rawane et al

in Annals of the Institute of Statistical Mathematics (2014)

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See detailEstimation of a general parametric location in censored regression
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Exploring research frontiers in contemporary statistics and econometrics - A Festschrift for Léopold Simar (2012)

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See detailQuantile regression in nonparametric location-scale models with censored data
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

Conference (2011, December)

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See detailEstimation of the Error Distribution in a Semiparametric Transformation Model
Heuchenne, Cédric ULg; Samb, Rawane; Van Keilegom, Ingrid

Scientific conference (2011, November 15)

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See detailLikelihood based inference for semi-competing risks
Heuchenne, Cédric ULg; Legrand, Catherine; Laurent, Stéphane ULg et al

E-print/Working paper (2011)

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See detailEstimating the residual distribution in a semiparametric transformation model.
Heuchenne, Cédric ULg; Samb, Rawane; Van Keilegom, Ingrid

E-print/Working paper (2011)

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See detailEstimation of the error density in a semi-parametric transformation model.
Samb, Rawane; Heuchenne, Cédric ULg; Van Keilegom, Ingrid

E-print/Working paper (2011)

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See detailEstimation in nonparametric location-scale regression models with censored data
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Annals of the Institute of Statistical Mathematics (2010), 62

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See detailGoodness-of-fit tests for the error distribution in nonparametric regression
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Computational Statistics & Data Analysis (2010), 54

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See detailCensparreg
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

Software (2010)

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See detailSemiparametric inference in general heteroscedastic regression models
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Proceedings of the Sixth Petersburg Workshop on Simulations (2009)

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See detailPolynomial regression with censored data based on preliminary nonparametric estimation
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Annals of the Institute of Statistical Mathematics (2007), 59(2), 273-297

Consider the polynomial regression model Y = (beta)0 + beta(1) X + center dot center dot center dot beta X-p(p) + sigma (X)epsilon, where sigma(2)(X) = Var(Y vertical bar X) is unknown, and epsilon is ... [more ▼]

Consider the polynomial regression model Y = (beta)0 + beta(1) X + center dot center dot center dot beta X-p(p) + sigma (X)epsilon, where sigma(2)(X) = Var(Y vertical bar X) is unknown, and epsilon is independent of X and has zero mean. Suppose that Y is subject to random right censoring. A new estimation procedure for the parameters beta(0), center dot center dot center dot, beta (p) is proposed, which extends the classical least squares procedure to censored data. The proposed method is inspired by the method of Buckley and James (1979, Biometrika, 66, 429-436), but is, unlike the latter method, a noniterative procedure due to nonparametric preliminary estimation of the conditional regression function. The asymptotic normality of the estimators is established. Simulations are carried out for both methods and they show that the proposed estimators have usually smaller variance and smaller mean squared error than Buckley-James estimators. The two estimation procedures are also applied to a medical and a astronomical data set. [less ▲]

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See detailLocation estimation in nonparametric regression with censored data
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Journal Of Multivariate Analysis (2007), 98(8), 1558-1582

Consider the heteroscedastic model Y =m (X) +sigma(X)epsilon, where epsilon and X are independent, Y is subject to right censoring, m (center dot) is an unknown but smooth location function (like e.g ... [more ▼]

Consider the heteroscedastic model Y =m (X) +sigma(X)epsilon, where epsilon and X are independent, Y is subject to right censoring, m (center dot) is an unknown but smooth location function (like e.g. conditional mean, median, trimmed mean...) and sigma(center dot) an unknown but smooth scale function. In this paper we consider the estimation of m(center dot) under this model. The estimator we propose is a Nadaraya-Watson type estimator, for which the censored observations are replaced by 'synthetic' data points estimated under the above model. The estimator offers an alternative for the completely nonparametric estimator of m (center dot), which cannot be estimated consistently in a completely nonparametric way, whenever high quantiles of the conditional distribution of Y given X = x are involved. We obtain the asymptotic properties of the proposed estimator of m (x) and study its finite samplebehaviour in a simulation study. The method is also applied to a study of quasars in astronomy. (c) 2007 Elsevier Inc. All rights reserved. [less ▲]

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See detailPolynomial regression with censored data based on preliminary nonparametric estimation.
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Annals of the Institute of Statistical Mathematics (2007), 59

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See detailNonlinear regression with censored data
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Technometrics (2007), 49(1), 34-44

Suppose that the random vector (X, Y) satisfies the regression model Y = m(X) + sigma(X)epsilon, where m(.) = E(Y vertical bar.) belongs to some parametric class (m(theta)(.):theta is an element of Theta ... [more ▼]

Suppose that the random vector (X, Y) satisfies the regression model Y = m(X) + sigma(X)epsilon, where m(.) = E(Y vertical bar.) belongs to some parametric class (m(theta)(.):theta is an element of Theta) of regression functions, sigma(2)(.) = var(Y vertical bar.) is unknown, and e is independent of X. The response Y is subject to random right censoring, and the covariate X is completely observed. A new estimation procedure for the true, unknown parameter vector theta(0) is proposed that extends the classical least squares procedure for nonlinear regression to the case where the response is subject to censoring. The consistency and asymptotic normality of the proposed estimator are established. The estimator is compared through simulations with an estimator proposed by Stute in 1999, and both methods are also applied to a fatigue life dataset of strain-controlled materials. [less ▲]

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See detailNonparametric censored regression using preliminary kernel smoothing
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Proceedings of the 6th World Congress of the Bernoulli Society for Mathematical Statistics and Probability and the 67th Annual Meeting of the IMS (2004)

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See detailEstimation in censored linear regression via preliminary smoothing
Heuchenne, Cédric ULg; Van Keilegom, Ingrid

in Bulletin of the International Statistical Institute, 54th Session (2003)

Detailed reference viewed: 34 (5 ULg)