References of "Heuchenne, Cédric"
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See detailStrong uniform consistency results of the weighted average of conditional artificial data points
Heuchenne, Cédric ULg

in Journal of Statistical Planning & Inference (2008), 138(5), 1496-1515

In this paper, we study strong uniform consistency of a weighted average of artificial data points. This is especially useful when information is incomplete (censored data, missing data ...). In this case ... [more ▼]

In this paper, we study strong uniform consistency of a weighted average of artificial data points. This is especially useful when information is incomplete (censored data, missing data ...). In this case, reconstruction of the information is often achieved nonparametrically by using a local preservation of mean criterion for which the corresponding mean is estimated by a weighted average of new data points. The present approach enlarges the possible scope for applications beyond just the incomplete data context and can also be useful to treat the estimation of the conditional mean of specific functions of complete data points. As a consequence, we establish the strong uniform consistency of the Nadaraya - Watson [Nadaraya, E.A., 1964. On estimating regression. Theory Probab. Appl. 9, 141 - 142; Watson, G.S., 1964. Smooth regression analysis. Sankhya Ser. A 26, 359 - 372] estimator for general transformations of the data points. This result generalizes the one of Hardle et al. [Strong uniform consistency rates for estimators of conditional functionals. Ann. Statist. 16, 1428 - 1449]. In addition, the strong uniform consistency of a modulus of continuity will be obtained for this estimator. Applications of those two results are detailed for some popular estimators. (c) 2007 Elsevier B.V. All rights reserved. [less ▲]

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See detailGoodness-of-fit tests for censored regression based on artificial data points
González Manteiga, Wenceslao; Heuchenne, Cédric ULg; Sánchez Sellero, Cesar

Report (2008)

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See detailThe determinants of CDS prices: an industry-based investigation
Sougné, Danielle ULg; Heuchenne, Cédric ULg; Hübner, Georges ULg

in Wagner, Niklas (Ed.) Credit Risk: Models, Derivatives and Management. Empirical Studies and Analysis. Financial Mathematics Series. (2008)

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See detailParametric conditional mean and variance testing with censored data
Heuchenne, Cédric ULg; González Manteiga, Wenceslao; Sánchez Sellero, Cesar

in H. Skiadas, Christos (Ed.) Recent Advances in Applied Stochastic Models and Data Analysis. (2007)

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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 detailParametric conditional mean and variance testing with censored data
Heuchenne, Cédric ULg; González Manteiga, Wenceslao; Sanchez Sellero, Cesar

in ASMDA2007 Proceedings (2007)

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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: 27 (5 ULg)