Maximum likelihood estimation in a partially observed stratified regression model with censored data

Amélie Detais (1) , Jean-François Dupuy (1)

1 IMT - Institut de Mathématiques de Toulouse UMR5219

The stratified proportional intensity model generalizes Cox's proportional intensity model by allowing different groups of the population under study to have distinct baseline intensity functions. In this article, we consider the problem of estimation in this model when the variable indicating the stratum is unobserved for some individuals in the studied sample. In this setting, we construct nonparametric maximum likelihood estimators for the parameters of the stratified model and we establish their consistency and asymptotic normality. Consistent estimators for the limiting variances are also obtained.

Mots clés: Asymptotic normality; Consistency; Missing data; Nonparametric maximum likelihood; Right-censored failure time data; Stratified proportional intensity model; Variance estimation.; Variance estimation

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Amélie Detais, Jean-François Dupuy. Maximum likelihood estimation in a partially observed stratified regression model with censored data. 2008. ⟨hal-00277164⟩