Valentin Patilea

Product-limit estimators of the survival function with twice censored data

A model for competing (resp. complementary) risks survival data where the failure time can be left (resp. right) censored is proposed. Product-limit estimators for the survival functions of the individual risks are derived. We deduce the strong convergence of our estimators on the whole real half-line without any additional assumptions and their asymptotic normality under conditions concerning only the observed distribution. When the observations are generated according to the double censoring model introduced by Turnbull, the product-limit estimators represent upper and lower bounds for Turnbulls estimator.

Nonparametric lack-of-fit tests for parametric mean-regression models with censored data

We developed two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weighted least squares approaches for estimating the parameters of a (non)linear regression model under censoring. The asymptotic critical values of our tests are given by the quantiles of the standard normal law. The tests are consistent against fixed alternatives, local Pitman alternatives and uniformly over alternatives in Holder classes of functions of known regularity.

A uniform Berry-Esseen theorem on M¡estimators for geometrically ergodic Markov chains

Let

One for all and all for one: regression checks with many regressors

\{X_{n}\}_{n\geq 0}

Synthetic data based nonparametric testing of parametric mean-regression models with censored data

be a

Breaking the curse of dimensionality in nonparametric testing

V

Smooth minimum distance estimation and testing with conditional estimating equations: Uniform in bandwidth theory

-geometrically ergodic Markov chain. Given some real-valued functional

Nonlinear Censored Regression Using Synthetic Data.

F

Convex models, MLE and misspecification

, define

Single index regression models in the presence of censoring depending on the covariates

M_{n}(\alpha):=n^{-1}\sum^{n}_{k=1} F(\alpha, X_{k-1}, X_{k}), \alpha \in \mathcal A \subset \mathbb R