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See detailEstimation of the error distribution in nonparametric regression with cross-sectional data
Heuchenne, Cédric ULg; Laurent, Géraldine ULg

E-print/Working paper (2014)

In this article, we study the nonparametric regression model Y=m(X)+varepsilon where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions, and the error varepsilon has zero mean ... [more ▼]

In this article, we study the nonparametric regression model Y=m(X)+varepsilon where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions, and the error varepsilon has zero mean and finite variance conditionally on X=x. The problem consists in estimating the cumulative distribution function of the error in a nonparametric way when the couple (X,Y) is obtained by cross-sectional sampling while the positive response Y can be right-censored. We propose a new estimator for the error distribution function based on the estimators of m(.) and sigma²(.) described in Heuchenne and Laurent 2014. A bootstrap procedure is developed to solve the critical problem of the smoothing parameter choice. We assess the performance of the proposed estimator through simulations. Finally, a data set based on the mortality of diabetics is analyzed. (Heuchenne Cédric and Laurent Géraldine, Nonparametric regression with cross-sectional data: an alternative to conditional product-limit estimators, 2014) [less ▲]

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See detailParametric conditional variance estimation in location-scale models with censored data
Heuchenne, Cédric ULg; Laurent, Géraldine ULg

E-print/Working paper (2014)

Suppose the random vector (X,Y) satisfies the regression model Y=m(X)+sigma(X)*varepsilon, where m(.)=E(Y|.), sigma²(.)=Var(Y|.) belongs to some parametric class {sigma _theta(.): theta in Theta} and ... [more ▼]

Suppose the random vector (X,Y) satisfies the regression model Y=m(X)+sigma(X)*varepsilon, where m(.)=E(Y|.), sigma²(.)=Var(Y|.) belongs to some parametric class {sigma _theta(.): theta in Theta} and varepsilon is independent of X. The response Y is subject to random right censoring and the covariate X is completely observed. A new estimation procedure is proposed for sigma_theta(.) when m(.) is unknown. It is based on nonlinear least squares estimation extended to conditional variance in the censored case. The consistency and asymptotic normality of the proposed estimator are established. The estimator is studied via simulations and an important application is devoted to fatigue life data analysis. [less ▲]

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See detailNonparametric regression with cross-sectional data: an alternative to conditional product-limit estimators
Heuchenne, Cédric ULg; Laurent, Géraldine ULg

E-print/Working paper (2014)

Suppose the random vector (X,Y) satisfies the nonparametric regression model Y=m(X)+varepsilon, where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions and the error varepsilon ... [more ▼]

Suppose the random vector (X,Y) satisfies the nonparametric regression model Y=m(X)+varepsilon, where m(x)=E[Y|X=x] and sigma²(x)=Var[varepsilon|X=x] are unknown smooth functions and the error varepsilon has zero mean and finite variance conditionally on X=x. The pair (X,Y) is obtained by cross-sectional sampling involving left-truncated and right-censored responses. The considered model is completely nonparametric but the conditional truncation distribution is assumed to be known. The novelty of this work is twofold: first, it extends the results on cross-sectional data to the conditional case and second, it generalizes the length bias results in the conditional case to right censoring and to any truncation distribution. New estimators for m(.) and sigma²(.) are constructed and relevant tools are used to quickly provide the main asymptotic properties for this kind of estimators. Extensive simulations are carried out and show that the new estimators outperform classical nonparametric estimators for left-truncated and right-censored data (when the truncation model is known). Finally, a data set on the mortality of diabetics is analyzed. [less ▲]

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See detailA Statistically adaptive sampling policy to the Hotelling's T2 Control Chart: Markov Chain Approach
Seif, A.; Faraz, Alireza ULg; Heuchenne, Cédric ULg et al

in Communications in Statistics : Theory & Methods (2014)

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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)

Detailed reference viewed: 33 (8 ULg)
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See detailA new methodological approach for error distributions selection
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Conference (2013, December 15)

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We ... [more ▼]

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we do not use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure: the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures tests based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behaviour in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲]

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See detailA new methodological approach for error distributions selection
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Scientific conference (2013, November)

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We ... [more ▼]

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we do not use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure: the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures tests based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behaviour in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲]

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See detailDynamic Accelerated Failure Time Model with Endogeneity and Heterogeneity: a Control Function approach
Tiwari, Amaresh Kumar ULg; Heuchenne, Cédric ULg

Conference (2013, August)

We develop a control function method to estimate an Accelerated Failure Time (AFT) model with multiple states, where we account for state dependence, heterogeneity, and endogeneity of covariates. In ... [more ▼]

We develop a control function method to estimate an Accelerated Failure Time (AFT) model with multiple states, where we account for state dependence, heterogeneity, and endogeneity of covariates. In accounting for state dependency in the structural AFT model and endogeneity of covariates through control functions, we are faced with predetermined covariates in the first stage treatment choice equation, which is a system of regressions for panel data. A concentrated likelihood method has been proposed to estimate a system of regressions with predetermined covariates. The control functions are based on "expected a posteriori" (EAP) values of the correlated random effects, and unlike alternative control function approaches, our approach allows for general instruments. [less ▲]

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See detailDouble-objective Economic Statistical Design of the Adaptive T2 Control Charts
Faraz, Alireza ULg; Heuchenne, Cédric ULg; Saniga, Erwin et al

Conference (2013, July 09)

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

in European Journal of Operational Research (2013), 228(1), 282289

Delivery chains are concerned with the delivery of goods and services to customers within a specific time interval; this time constraint is added to the usual consumer demand for product or service ... [more ▼]

Delivery chains are concerned with the delivery of goods and services to customers within a specific time interval; this time constraint is added to the usual consumer demand for product or service quality. In this context, we address the idea of using process control tools to monitor this key variable of delivery time. In applications, there are usually several production and delivery sites and a variety of different ways to transport, treat and provide goods and services; that makes the problem multivariate in nature. We therefore propose to control the process using multivariate T2 control charts economically designed with the addition of statistical constraints, a design method called economic-statistical design. We illustrate the application in general through an illustrative example. [less ▲]

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See detailConditional Asset Allocation: Does Market Wide liquidity Matter?
Bazgour, Tarik ULg; Sougné, Danielle ULg; Heuchenne, Cédric ULg

Scientific conference (2013, June)

Detailed reference viewed: 31 (17 ULg)
See detailNew issues for the Goodness-of-fit test of the error distribution : a comparison between Sinh-arscinh and Generalized Hyperbolic distribution
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Scientific conference (2013, April 30)

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional ... [more ▼]

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional estimation and model selection procedures (Berk-Jones (1978) tests, Sarno and Valente (2004) hypothesis testing, Diks et al. (2011) weighting method), based on the local volatility estimator of Mercurio and Spokoiny (2004) and the bootstrap methodology to compare the fit performances of candidate density functions. In particular, we introduce the sinh-arcsinh distributions (Jones and Pewsey, 2009) and we show that this family of density functions provides better bootstrap IMSE and better weighted Kullback-Leibler distances. [less ▲]

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See detailNew issues for the Goodness-of-fit test of the error distribution : a comparison between Sinh-arcsinh and Generalized Hyperbolic distributions
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Scientific conference (2013, April 19)

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional ... [more ▼]

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional estimation and model selection procedures (Berk-Jones (1978) tests, Sarno and Valente (2004) hypothesis testing, Diks et al. (2011) weighting method), based on the local volatility estimator of Mercurio and Spokoiny (2004) and the bootstrap methodology to compare the fit performances of candidate density functions. In particular, we introduce the sinh-arcsinh distributions (Jones and Pewsey, 2009) and we show that this family of density functions provides better bootstrap IMSE and better weighted Kullback-Leibler distances. [less ▲]

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See detailConditional Asset Allocation: Does Market Wide liquidity Matter?
Bazgour, Tarik ULg; Sougné, Danielle ULg; Heuchenne, Cédric ULg

Scientific conference (2013, April 17)

Detailed reference viewed: 21 (9 ULg)
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See detailConditional Asset Allocation: Does Market Wide liquidity Matter?
Bazgour, Tarik ULg; Sougné, Danielle ULg; Heuchenne, Cédric ULg

Scientific conference (2013, March 07)

Detailed reference viewed: 32 (13 ULg)
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See detailEstimation from cross-sectional data under a semiparametric truncation model
de Uña-Álvarez, Jacobo; Heuchenne, Cédric ULg; Laurent, Géraldine ULg

E-print/Working paper (2013)

Cross-sectional sampling is often used when investigating inter-event times, resulting in left-truncated and right-censored data. In this paper we consider a semiparametric truncation model in which the ... [more ▼]

Cross-sectional sampling is often used when investigating inter-event times, resulting in left-truncated and right-censored data. In this paper we consider a semiparametric truncation model in which the truncating variable is assumed to belong to a certain parametric family, while nothing is assumed on lifetime and censoring distributions. The novelty of this work is in the fact that it introduces estimators of this semiparametric model in the presence of censoring. Two alternative methods are considered, based on conditional and full likelihood considerations. Asymptotic representations of the estimators for the lifetime distribution are obtained, and their weak convergence is established. The finite sample performance of the new estimators is explored through simulations, and two real data illustrations are provided. One of the conclusions of our research is that both estimators perform better than Wang's NPMLE when the parametric family for the truncation variable is valid) in the sense of the integrated mean squared error, and that the full likelihood approach is preferable to the conditional likelihood approach for the estimation of the lifetime distribution but not necessarily for the estimation of the truncation distribution. [less ▲]

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See detailPenalized Pro led Semiparametric Estimating Functions
Wang, Lan; Kai, Bo; Heuchenne, Cédric ULg et al

in Electronic Journal of Statistics (2013), 7

Detailed reference viewed: 14 (1 ULg)