39 citations · 122 across the 23 of their papers we have counts for
9 papers · 1 filter
Adaptive functional principal components analysis
Sunny G. W. Wang, Valentin Patilea, Nicolas Klutchnikoff
Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although s…
A 2-step estimation procedure for semiparametric mixture cure models
Eni Musta, Valentin Patilea, Ingrid Van Keilegom
Cure models have been developed as an alternative modelling approach to conventional survival analysis in order to account for the presence of cured subjects that will never experi…
A presmoothing approach for estimation in semiparametric mixture cure models
Eni Musta, Valentin Patilea, Ingrid Van Keilegom
A challenge when dealing with survival analysis data is accounting for a cure fraction, meaning that some subjects will never experience the event of interest. Mixture cure models…
Wilks' theorem for semiparametric regressions with weakly dependent data
Marie Du Roy de Chaumaray, Matthieu Marbac, Valentin Patilea
The empirical likelihood inference is extended to a class of semiparametric models for stationary, weakly dependent series. A partially linear single-index regression is used for t…
Orthogonal Impulse Response Analysis in Presence of Time-Varying Covariance
Valentin Patilea, Hamdi Raïssi
In this paper the orthogonal impulse response functions (OIRF) are studied in the non-standard, though quite common, case where the covariance of the error vector is not constant i…
A likelihood-based approach for cure regression models
Kevin Burke, Valentin Patilea
We propose a new likelihood-based approach for estimation, inference and variable selection for parametric cure regression models in time-to-event analysis under random right-censo…