hermite_estimator_univar: A class to sequentially estimate univariate pdfs, cdfs and...

In hermiter: Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)

A class to sequentially estimate univariate pdfs, cdfs and quantile functions

Description

This method constructs an S3 object with associated methods for univariate nonparametric estimation of pdfs, cdfs and quantiles.

Usage

hermite_estimator_univar(
  N = 50,
  standardize = TRUE,
  exp_weight_lambda = NA,
  observations = c()
)

Arguments

N	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.
standardize	A boolean value. Determines whether the observations are standardized, a transformation which often improves performance.
exp_weight_lambda	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.
observations	A numeric vector. A vector of observations to be incorporated into the estimator.

Details

The hermite_estimator_univar class allows the sequential or one-pass batch estimation of the full probability density function, cumulative distribution function and quantile function. 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.

Value

An S3 object of class hermite_estimator_univar, with methods for density function, distribution function and quantile function estimation.

Author(s)

Michael Stephanou <[e-mail]>

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