# 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 Spearman's Correlation (Bivariate)

## 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. Note that this method is only applicable to the univariate Hermite estimator i.e. est_type = "univariate".

## Usage

 1 quant(this, p, algorithm = "interpolate", accelerate_series = TRUE) 

## Arguments

 this 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.

## Value

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

## Examples

 1 2 3 4 hermite_est <- hermite_estimator(N = 10, standardize = TRUE, est_type="univariate") hermite_est <- update_batch(hermite_est, rnorm(30)) quant_est <- quant(hermite_est, c(0.25, 0.5, 0.75)) 

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