Jun 30, 2014 · In this paper, we give a direct method to approximate the density derivative without estimating the density itself.

In this paper, we give a direct method to approximate the den- sity derivative without estimating the den- sity itself. Our proposed estimator allows analytic ...

The proposed method provides computationally efficient estimation for the derivatives of any order on multidimensional data with a hyperparameter tuning method ...

Jun 1, 2016 · Abstract. Estimating the derivatives of probability density functions is an essential step in statistical data analysis.

In this paper, we give a direct method to approximate the density derivative without estimating the density itself.

Estimating the derivatives of probability density functions is an essen- tial step in statistical data analysis. A naive approach to estimate the.

A key technical ingredient in SCMS is to accurately estimate the ratios of the density derivatives to the density. SCMS takes a three-step approach for this ...

This paper proposes three new methods for unconstrained bandwidth matrix selection for the multivariate kernel density derivative estimator, and explores their ...

The present paper studies the kernel type as a non-parametric estimation of the density function derivative connected with a highly mixing time series.

The proposed density-derivative estimator allows analytic and computationally efficient approximation of multi-dimensional high-order density derivatives, ...