hermite_estimator: A class to sequentially estimate univariate and bivariate...

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 and bivariate pdfs and cdfs along with quantile functions in the univariate setting and nonparametric correlations in the bivariate setting.

Description

The hermite_estimator class provides a unified interface to the univariate and bivariate Hermite series based estimators, leveraging generic methods and S3 dispatch. Methods are included for the sequential or one-pass batch estimation of the full probability density function and cumulative distribution function in the univariate and bivariate settings. Sequential or one-pass batch estimation methods are also provided for the full quantile function in the univariate setting along with the Spearman and Kendall correlation coefficients in the bivariate setting. Note that RcppParallel is utilized to speed up batch updating in the univariate case. If one wishes to switch to serial batch updating (typically slower), utilize options(hermiter.parallel = FALSE).

Usage

hermite_estimator(
  N = NA,
  standardize = TRUE,
  exp_weight_lambda = NA,
  est_type = "univariate",
  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.
est_type	A string value. Options are "univariate" or "bivariate".
observations	A numeric vector or a numeric matrix. Note that for univariate estimators, x is a numeric vector of observations to be incorporated. For bivariate estimators, x is a numeric matrix with n rows for n observations and 2 columns.

Value

An S3 object of class hermite_estimator_univar or hermite_estimator_bivar.

Author(s)

Michael Stephanou <[e-mail]>

Examples

## Not run: 
hermite_est <- hermite_estimator(N = 50, standardize = TRUE,
est_type="univariate")
hermite_est <- hermite_estimator(N = 50, standardize = TRUE,
est_type="univariate", observations = c(1,2,3))
hermite_est <- hermite_estimator(N = 30, standardize = TRUE, 
est_type="bivariate", observations = matrix(c(1,1,2,2,3,3), 
nrow=3, ncol=2,byrow=TRUE))

## End(Not run)

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