imp_sampC: imp_sampC

In bcallaway11/qrme: Quantile Regression with Measurement Error

imp_sampC

Description

return a vector of weights to be used in importance sampling note that, unlike mh_mcmcC, here the measurement error vector has already been drawn and all we need to do is compute weights

Usage

imp_sampC(Y, X, V, iters, drawsd, betmat, m, pi, mu, sig, tau)

Arguments

Y	vector of outcomes
X	matrix of covariates
V	vector of measurement errors
iters	number of Monte Carlo iterations
drawsd	the standard deviation for the standard normal draws in the MH algorithm
betmat	matrix of QR parameters
m	number of mixture components for measurement error
pi	mixture probabilities
mu	means of mixture components
sig	standard deviations of mixture components
tau	which values QR's have been estimated for

Value

vector of weights to be used in importance sampling

bcallaway11/qrme documentation built on May 16, 2024, 12:02 p.m.