Asymptotic Normality of Hill Estimator for Truncated Data

Asymptotic normality of Hill Estimator for truncated data

The problem of estimating the tail index from truncated data is addressed in ?. In that paper, a sample based (and hence random) choice of k is suggested, and it is shown that the choice leads to a consistent estimator of the inverse of the tail index. In this paper, the second order behavior of the Hill estimator with that choice of k is studied, under some additional assumptions. In the untru...

asymptotic normality of the truncation probability estimator for truncated dependent data

in some long term studies, a series of dependent and possibly truncated life-times may be observed. suppose that the lifetimes have a common marginal distribution function. in left-truncation model, one observes data (xi,ti) only, when ti ≤ xi. under some regularity conditions, we provide a strong representation of the ßn estimator of ß = p(ti ≤ xi), in the form of an average of random variable...

Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data

Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

Asymptotic normality of Powell’s kernel estimator

In this paper, we establish asymptotic normality of Powell’s kernel estimator for the asymptotic covariance matrix of the quantile regression estimator for both i.i.d. and weakly dependent data. As an application, we derive the optimal bandwidth that minimizes the approximate mean squared error of the kernel estimator.

Asymptotic normality of randomly truncated stochastic algorithms