References of "Heuchenne, Cédric"
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See detailTesting for one-sided alternatives in nonparametric censored regression
Heuchenne, Cédric ULg; Pardo Fernandez, J. C.

Scientific conference (2011, February 08)

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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 detailIntroduction of the asymptotic study of the estimation of the error distribution in right censored and selection biased regression models
Laurent, Géraldine ULg; Heuchenne, Cédric ULg

Poster (2010, October)

Consider the regression model Y = m(X) + σ(X) Ɛ where m(X) = E[Y|X] and σ²(X)=Var[Y|X] are unknown smooth functions and the error Ɛ , with unknown distribution, is independent of the covariate X. The pair ... [more ▼]

Consider the regression model Y = m(X) + σ(X) Ɛ where m(X) = E[Y|X] and σ²(X)=Var[Y|X] are unknown smooth functions and the error Ɛ , with unknown distribution, is independent of the covariate X. The pair (X;Y) is subject to generalized bias selection and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error Ɛ , where the estimators of m(.) and σ²(.) are obtained by extending the conditional estimation methods introduced in de Uña-Alvarez and Iglesias-Perez (2008). The asymptotic properties of the functions m(.) and σ(.) are obtained. A bootstrap technique is proposed to select the smoothing parameter involved in the procedure. This method is studied via extended simulations and applied to real unemployment data. [less ▲]

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See detailComputational treatment of the error distribution in nonparametric regression with right-censored and selection-biased data
Laurent, Géraldine ULg; Heuchenne, Cédric ULg

Conference (2010, August 24)

Consider the regression model Y = m(X) + σ(X) Ɛ , where m(X) = E[Y|X] and σ²(X) = Var[Y|X] are unknown smooth functions and the error Ɛ (with unknown distribution) is independent of X. The pair (X;Y) is ... [more ▼]

Consider the regression model Y = m(X) + σ(X) Ɛ , where m(X) = E[Y|X] and σ²(X) = Var[Y|X] are unknown smooth functions and the error Ɛ (with unknown distribution) is independent of X. The pair (X;Y) is subject to generalized selection bias and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error Ɛ , and develop a bootstrap technique to select the smoothing parameter involved in the procedure. The estimator is studied via extended simulations and applied to real unemployment data. [less ▲]

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See detailComputational study of the error distribution in right-censored and selection-biased regression models
Laurent, Géraldine ULg; Heuchenne, Cédric ULg

Conference (2010, May 18)

Consider the regression model Y = m(X) + σ(X) Ɛ where m(X) =E [Y|X] and σ²(X) = Var [Y|X] are unknown smooth functions and the error Ɛ, with unknown distribution, is independent of X. The pair (X,Y) is ... [more ▼]

Consider the regression model Y = m(X) + σ(X) Ɛ where m(X) =E [Y|X] and σ²(X) = Var [Y|X] are unknown smooth functions and the error Ɛ, with unknown distribution, is independent of X. The pair (X,Y) is subject to generalized selection bias and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error Ɛ, and develop a bootstrap technique to select the smoothing parameter involved in the procedure. The estimator is studied via extended simulations and applied to real unemployment data. [less ▲]

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See detailThe Optimal Design of the VSI T2 Control Chart
Faraz, Alireza ULg; Kazemzadeh, R. B.; Heuchenne, Cédric ULg et al

in Journal of Iranian Statistical Society (2010), 9(1), 1-19

Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and ... [more ▼]

Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and statistically optimal design of the VSI T2 control chart for monitoring the process mean vector is investigated. The cost model proposed by Lorenzen and Vance (1986) is minimized through a genetic algorithm (GA) approach. Then the effects of the costs and operating parameters on the optimal design (OD) of the chart parameters and resulting operating loss through a fractional factorial design is systematically studied and finally, based on the ANOVA results, a Meta model to facilitate implementation in industry is proposed to determine the OD of the VSI T2 control chart parameters from the process and cost parameters [less ▲]

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See detailA Meta Model to Optimal Design the VSI T2 Chart
Faraz, Alireza ULg; Heuchenne, Cédric ULg; Saniga, E.

Report (2010)

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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 detailTesting for one-sided alternatives in nonparametric censored curve comparison
Heuchenne, Cédric ULg; Pardo Fernandez, Juan-Carlos

in Proceedings of the 28th European Meeting of Statisticians. (2010)

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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 detailComputational treatment of the error distribution in nonparametric regression with right-censored and selection-biased data
Heuchenne, Cédric ULg; Laurent, Géraldine ULg

in Proceedings of the 19th International Conference on Computational Statistics (2010)

Detailed reference viewed: 36 (12 ULg)
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See detailEstimation of the error distribution in right censored and selection biased regression models
Laurent, Géraldine ULg; Heuchenne, Cédric ULg

Conference (2009, October 15)

Consider the location-scale regression model Y=m(X) + σ(X) Ɛ where the error Ɛ is independent of the covariate X and where m and σ are unknown smooth functions. The pair (X; Y ) is subject to generalized ... [more ▼]

Consider the location-scale regression model Y=m(X) + σ(X) Ɛ where the error Ɛ is independent of the covariate X and where m and σ are unknown smooth functions. The pair (X; Y ) is subject to generalized bias selection and the response to right censoring. We construct an estimator for the cumulative distribution function of the error Ɛ, and develop a bootstrap procedure to select the smoothing parameter involved in the procedure. This method is studied via extension simulations and applied to real unemployment data. [less ▲]

Detailed reference viewed: 20 (11 ULg)
See detailSemiparametric inference in general heteroscedastic regression models.
Heuchenne, Cédric ULg

Scientific conference (2009, June 09)

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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)

Detailed reference viewed: 18 (10 ULg)
See detailNonparametric Mean Preservation in Censored Regression
Heuchenne, Cédric ULg

Book published by VDM Verlag (2009)

The aim of this book is to estimate the conditional mean of some functions depending on the response variable Y (moments, distributions...) in regression models where this response is possibly censored ... [more ▼]

The aim of this book is to estimate the conditional mean of some functions depending on the response variable Y (moments, distributions...) in regression models where this response is possibly censored. In parametric regression, polynomial and nonlinear conditional means are estimated in a new way while, in nonparametric regression, some new estimators are provided to approximate general L-functionals (conditional mean, trimmed mean, quantiles...). The ideas developed in those methods lead to establish more general results in nonparametric estimation of the conditional mean of functions depending on Y and other variables and where the response can follow other schemes of incomplete data (not only censored but also missing or length-biased data). For each procedure, asymptotic properties are established while finite sample behavior is studied via simulations. Examples from a variety of areas highlight the interest of using the proposed methodologies in practice. [less ▲]

Detailed reference viewed: 53 (14 ULg)