hermite_estimator_bivar: A class to sequentially estimate bivariate pdfs, cdfs and...

View source: R/hermite_estimator_bivar.R

hermite_estimator_bivarR Documentation

A class to sequentially estimate bivariate pdfs, cdfs and nonparametric correlations


This method constructs an S3 object with methods for nonparametric estimation of bivariate pdfs and cdfs along with nonparametric correlations.


  N = 30,
  standardize = TRUE,
  exp_weight_lambda = NA,
  observations = c()



An integer between 0 and 75. The upper bound has been chosen as a value that yields an estimator that is reasonably fast and that remains robust to numerical issues. The Hermite series based estimator is truncated at N+1 terms.


A boolean value. Determines whether the observations are standardized, a transformation which often improves performance.


A numerical value between 0 and 1. This parameter controls the exponential weighting of the Hermite series based estimator. If this parameter is NA, no exponential weighting is applied.


A numeric matrix. A matrix of bivariate observations to be incorporated into the estimator. Each row corresponds to a single bivariate observation.


The hermite_estimator_bivar class allows the sequential or one-pass batch estimation of the full bivariate probability density function and cumulative distribution function along with the Spearman's rank correlation coefficient. It is well suited to streaming data (both stationary and non-stationary) and to efficient estimation in the context of massive or distributed data sets. Indeed,estimators constructed on different subsets of a distributed data set can be consistently merged.


An S3 object of class hermite_estimator_bivar, with methods for density function and distribution function estimation along with Spearman's rank correlation estimation.


Michael Stephanou <michael.stephanou@gmail.com>

hermiter documentation built on May 31, 2023, 6:30 p.m.