esr_rho_lp

Matrix of covariates. Note: intercept needs to be added manually

alpha

1, 2 (see <a rd-options="" href="/link/G1_fun?package=esreg&version=0.3.1" data-mini-rdoc="esreg::G1_fun">G1_fun</a>)

1, 2, 3, 4, 5 (see <a rd-options="" href="/link/G2_curly_fun?package=esreg&version=0.3.1" data-mini-rdoc="esreg::G2_curly_fun">G2_curly_fun</a>, <a rd-options="" href="/link/G2_fun?package=esreg&version=0.3.1" data-mini-rdoc="esreg::G2_fun">G2_fun</a>)

Smooth approximation of the indicator function (0 is the indicator function)

delta

Returns the loss for the parameter vector b

internal

Simultaneous modeling of the quantile and the expected shortfall of a response variable given
a set of covariates, see Dimitriadis and Bayer (2017) <arXiv:1704.02213>.

Sebastian Bayer

esreg

Joint Quantile and Expected Shortfall Regression

Timo Dimitriadis

esr_rho_lp function

1, 2 (see <a rd-options='' href='G1_fun'>G1_fun</a>)

1, 2, 3, 4, 5 (see <a rd-options='' href='G2_curly_fun'>G2_curly_fun</a>, <a rd-options='' href='G2_fun'>G2_fun</a>)

esr_rho_lp: Joint (VaR, ES) loss for a linear predictor

Description

Usage

Arguments