Likelihood based inference for semi-competing risks

in Communications in Statistics : Simulation & Computation (2013)

Quantile regression in nonparametric location-scale models with censored data

Conference (2011, December)

Likelihood based inference for semi-competing risks

E-print/Working paper (2011)

Estimation of the error density in a semi-parametric transformation model.

E-print/Working paper (2011)

Goodness-of-fit tests for the error distribution in nonparametric regression

in Computational Statistics & Data Analysis (2010), 54

Semiparametric inference in general heteroscedastic regression models

in Proceedings of the Sixth Petersburg Workshop on Simulations (2009)

Location estimation in nonparametric regression with censored data

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 ▲]

Nonlinear regression with censored data

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 ▲]

Estimation in censored linear regression via preliminary smoothing

in Bulletin of the International Statistical Institute, 54th Session (2003)