References of "Heuchenne, Cédric"
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See detailMULTIOBJECTIVE DESIGN OF CONTROL CHARTS
Faraz, Alireza ULg; Heuchenne, Cédric ULg

Conference (2011, September 24)

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See detailMonitoring delivery chains in a supply chain using multivariate control charts
Faraz, Alireza ULg; Heuchenne, Cédric ULg; Saniga, Erwin et al

Conference (2011, July 13)

The theory of the delivery chain considers the delivery of goods and services to customers within a specific time interval. Nowadays, organizations are focusing to satisfy their customers’ demands not ... [more ▼]

The theory of the delivery chain considers the delivery of goods and services to customers within a specific time interval. Nowadays, organizations are focusing to satisfy their customers’ demands not only to meet the expectations for products quality but also in delivery times through managing delivery chains. Obviously it is desirable that a company has minimum delivery time of goods and services to its customers. Therefore, establishing economic and reliable control charts for monitoring the key characteristics of delivery chain is of great importance especially for managers to improve the whole delivery chains performance. On the other hand, as we shall see in the present paper, the performance of a delivery chain is multivariate in nature because customers do not evaluate a delivery performance with a univariate mindset and also there are usually several production and delivery sites, and variety of different methods of transportation of goods and services. In this paper, we propose a relatively new application of the economic statistical design of the multivariate T2 control chart to monitor the delivery chain performance and it is illustrated through a case study in the TNT express mail in Iran. [less ▲]

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See detailError distribution estimation in right censored and selection biased location-scale models
Laurent, Géraldine ULg; Heuchenne, Cédric ULg

Poster (2011, June 23)

Suppose the random vector (X;Y) satis es the regression model Y = m(X)+sigma(X)*epsilon where m(X) = E[Y|X] and sigma²(X) = Var[Y|X] are unknown smooth functions and the error epsilon, with unknown ... [more ▼]

Suppose the random vector (X;Y) satis es the regression model Y = m(X)+sigma(X)*epsilon where m(X) = E[Y|X] and sigma²(X) = Var[Y|X] are unknown smooth functions and the error epsilon, with unknown distribution, is independent of the covariate X. The pair (X;Y) is subject to generalized selection biased and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error epsilon, where the estimators of m(.) and sigma²(.) are obtained by extending the conditional estimation methods introduced in de Uña-Alvarez and Iglesias-Perez (2010). The asymptotic properties of the proposed estimator are established. 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. Reference de UNA-ALVAREZ, J., IGLESIAS-PEREZ, M.C. (2010): Nonparametric estimation of a conditional distribution from length-biased data. Annals of the Institute of Statistical Mathematics, Vol. 62, 323-341. [less ▲]

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See detailNonparametric regression with parametric selection bias
Heuchenne, Cédric ULg; Laurent, Géraldine ULg

in Proceedings of the 14th Conference of the ASMDA International Society (ASMDA2011) (2011, June)

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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)

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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: 22 (11 ULg)