hermiter (version 2.3.1)

quant: 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)

Value

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

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.

Examples

if (FALSE) {
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))
}