The effects of over- and underfitting the regression model is studied for M-estimators. Applying nowadays already classic tool, namely the asymptotic linearity of M-statistics, the Bahadur representation of M-estimators in over- and underfitted model is found. It allows to establish conditions...

We consider a large class of transformation models introduced by Gu et al. (2005)  [14]. They proposed an estimation procedure for calculating the maximum partial marginal likelihood estimator (MPMLE) of regression parameters. A big advantage of MPMLE is that it avoids estimating two...

In this paper we study the strong and weak convergence with rates for the estimators of the conditional distribution function as well as conditional cumulative hazard rate function for a left truncated and right censored model. It is assumed that the lifetime observations with multivariate...

Under the condition that the observations, which come from a high-dimensional population (X,Y), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric M-estimator for the unknown...

Under the condition that the observations, which come from a high-dimensional population (X,Y), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric M-estimator for the unknown...

Under the condition that the observations, which come from a high-dimensional population (<I>X,Y</I>), are strongly stationary and strongly-mixing, through using the local linear method, we investigate, in this paper, the strong Bahadur representation of the nonparametric <I>M</I>-estimator for the unknown...</i></i>