quant: Estimates the quantiles at a vector of probability values

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)

Estimates the quantiles at a vector of probability values

Description

This method utilizes the estimator (13) in paper Stephanou, Michael, Varughese, Melvin and Iain Macdonald. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, with some modifications to improve the stability of numerical root finding when using the bisection algorithm. Note that this method is only applicable to the univariate Hermite estimator i.e. est_type = "univariate".

Usage

quant(h_est_obj, p, algorithm = "interpolate", accelerate_series = TRUE)

Arguments

h_est_obj	A hermite_estimator_univar object.
p	A numeric vector. A vector of probability values.
algorithm	A string. Two possible values 'interpolate' which is faster but may be less accurate or 'bisection' which is slower but potentially more accurate.
accelerate_series	A boolean value. If set to TRUE, the series acceleration methods described in: Boyd, John P., and Dennis W. Moore. "Summability methods for Hermite functions." Dynamics of atmospheres and oceans 10.1 (1986): 51-62. are applied. If set to FALSE, then standard summation is applied.

Details

The object must be updated with observations prior to the use of this method.

Value

A numeric vector. The vector of quantile values associated with the probabilities p.

Examples

## Not run: 
hermite_est <- hermite_estimator(N = 10, standardize = TRUE, 
est_type="univariate", observations = rnorm(30))
quant_est <- quant(hermite_est, c(0.25, 0.5, 0.75))

## End(Not run)

hermiter documentation built on May 29, 2024, 2:09 a.m.