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

Description Usage Arguments Details Value Author(s) Examples

View source: R/hermite_estimator_univar.R

Description

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

Usage

1
hermite_estimator_univar(N = 30, standardize = TRUE, exp_weight_lambda = NA)

Arguments

N

An integer between 0 and 75. 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.

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 <michael.stephanou@gmail.com>

Examples

1
hermite_est <- hermite_estimator_univar(N = 30, standardize = TRUE)

hermiter documentation built on Nov. 17, 2021, 1:07 a.m.