# 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)

 quant R Documentation

## 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 31, 2023, 6:30 p.m.