fit_weighted_rqgpd: Fit a weighted quantile regression and gpd model

In gbstat/tailqr: 'Fit Mixture of Quantile Regression and GPDs'

Description

Estimate quantiles using mixture of quantile regression and GPDs. Use triangular weighting functions to tradeoff between QR fit and GPD fits.

Usage

fit_weighted_rqgpd(y, dates, b.df.lf, b.df.hf, b.b.formula, t.df.lf, t.df.hf,
  t.b.formula, taus, l.taus, u.taus, thresh.l.taus, thresh.u.taus,
  use.winter.shape = FALSE, pred.dates = NULL)

Arguments

y	response vector samples
dates	dates corresponding to response observations
b.df.lf	df for bulk low freq basis
b.df.hf	df for bulk high freq basis
b.b.formula	bulk formula involving b.lf and b.hf
t.df.lf	df for tail low freq basis
t.df.hf	df for tail high freq basis
t.b.formula	tail formula (simpler) involving b.lf and b.hf
taus	vector of intermediate (bulk) quantiles to estimate (between 0 and 1)
l.taus	vector of lower tail quantiles to estimate (between 0 and 1)
u.taus	vector of higher tail quantiles to estimate (between 0 and 1)
thresh.l.taus	lower threshold quantiles for defining collection of GPDs
thresh.u.taus	upper threshold quantiles for defining collection of GPDs
use.winter.shape	(logical) should a separate shape parameter be estimated for winter months?
pred.dates	(optional) vector of dates to make predictions on (if different from dates)

gbstat/tailqr documentation built on May 8, 2019, 5:42 p.m.