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Estimation of the error distribution in a semiparametric transformation model. Heuchenne, Cédric ; ; in Electronic Journal of Statistics (2015), 9 Detailed reference viewed: 13 (3 ULg)Estimating the error distribution function of a heteroskedastic nonparametric regression of cure model data ; Heuchenne, Cédric ; Conference (2015) Detailed reference viewed: 11 (1 ULg)Estimation of the error density in a semiparametric transformation model ; Heuchenne, Cédric ; et al in Annals of the Institute of Statistical Mathematics (2015), 67 Detailed reference viewed: 43 (10 ULg)Likelihood based inference for semi-competing risks Heuchenne, Cédric ; Laurent, Stéphane ; et al in Communications in Statistics : Simulation & Computation (2014), 43(5), 1112-1132 Detailed reference viewed: 61 (7 ULg)Estimation of a general parametric location in censored regression Heuchenne, Cédric ; in Exploring research frontiers in contemporary statistics and econometrics - A Festschrift for Léopold Simar (2012) Detailed reference viewed: 34 (3 ULg)Quantile regression in nonparametric location-scale models with censored data Heuchenne, Cédric ; Conference (2011, December) Detailed reference viewed: 30 (0 ULg)Estimation of the Error Distribution in a Semiparametric Transformation Model Heuchenne, Cédric ; ; Scientific conference (2011, November 15) Detailed reference viewed: 33 (1 ULg)Likelihood based inference for semi-competing risks Heuchenne, Cédric ; ; Laurent, Stéphane et al E-print/Working paper (2011) Detailed reference viewed: 32 (4 ULg)Estimating the residual distribution in a semiparametric transformation model. Heuchenne, Cédric ; ; E-print/Working paper (2011) Detailed reference viewed: 21 (3 ULg)Estimation of the error density in a semi-parametric transformation model. ; Heuchenne, Cédric ; E-print/Working paper (2011) Detailed reference viewed: 27 (2 ULg)Estimation in nonparametric location-scale regression models with censored data Heuchenne, Cédric ; in Annals of the Institute of Statistical Mathematics (2010), 62 Detailed reference viewed: 99 (25 ULg)Goodness-of-fit tests for the error distribution in nonparametric regression Heuchenne, Cédric ; in Computational Statistics & Data Analysis (2010), 54 Detailed reference viewed: 48 (21 ULg)Semiparametric inference in general heteroscedastic regression models Heuchenne, Cédric ; in Proceedings of the Sixth Petersburg Workshop on Simulations (2009) Detailed reference viewed: 27 (10 ULg)Polynomial regression with censored data based on preliminary nonparametric estimation Heuchenne, Cédric ; 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 ▲] Detailed reference viewed: 58 (16 ULg)Location estimation in nonparametric regression with censored data Heuchenne, Cédric ; 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 ▲] Detailed reference viewed: 61 (11 ULg)Polynomial regression with censored data based on preliminary nonparametric estimation. Heuchenne, Cédric ; in Annals of the Institute of Statistical Mathematics (2007), 59 Detailed reference viewed: 55 (26 ULg)Nonlinear regression with censored data Heuchenne, Cédric ; 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 ▲] Detailed reference viewed: 104 (9 ULg)Nonparametric censored regression using preliminary kernel smoothing Heuchenne, Cédric ; 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) Detailed reference viewed: 40 (6 ULg)Estimation in censored linear regression via preliminary smoothing Heuchenne, Cédric ; in Bulletin of the International Statistical Institute, 54th Session (2003) Detailed reference viewed: 41 (6 ULg) |
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