The properties of non-parametric kernel estimators for probability density function from two special classes are investigated. Each class is parametrized with distribution smoothness parameter. One the was introduced by Rosenblatt, another one in this paper. For case known parameter, rates mean square convergence optimal (on bandwidth) found. unknown estimation procedure parameter developed and...

various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. in the classical approaches,  making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. however, most of the times enough information about the probability distribution of studied variables is not available to the ...

We propose a wavelet based regression function estimator for the estimation of the regression function for a sequence of ?-missing random variables with a common one-dimensional probability density function. Some asymptotic properties of the proposed estimator based on block thresholding are investigated. It is found that the estimators achieve optimal minimax convergence rates over large class...

Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input subsequent decision making like ordering processes. In contrast to point estimates, such conditional mean underlying probability distribution, or confidence intervals, complete density functions allows investigate impact on operational metrics, which are important define business s...