EM.qr: Quantile Regression Using Asymmetric Laplace Distribution

Description Usage Arguments Value Author(s) References Examples

View source: R/EM.qr.r

Description

Return estimating the parameters in a quantile regression

Usage

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EM.qr(y, x = NULL, tau = NULL, error = 0.000001 , iter = 2000, envelope=FALSE)

Arguments

y

vector of responses

x

the design matrix

tau

the quantile to be estimated, this is generally a number strictly between 0 and 1.

error

the covergence maximum error

iter

maximum iterations of the EM algorithm.

envelope

confidence envelopes for a curve based on bootstrap replicates

Value

Estimated parameter for a quantile regression fit,standard error, log-likelihood.

Author(s)

Luis Benites Sanchez [email protected], Christian E. Galarza [email protected], Victor Hugo Lachos [email protected]

References

[1] Koenker, R. W. (2005). Quantile Regression, Cambridge U. Press.

[2] Yu, K. & Moyeed, R. (2001). Bayesian quantile regression. Statistics & Probability Letters, 54 (4), 437 to 447.

[3] Kotz, S., Kozubowski, T. & Podgorski, K. (2001). The laplace distribution and generalizations: A revisit with applications to communications, economics, engineering, and finance. Number 183. Birkhauser.

Examples

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data(ais, package="sn")
attach(ais)
sexInd <- (sex=="female") + 0
x      <- cbind(1,LBM,sexInd)
y      <- BMI
tau    <- 0.5

## EM.qr
EM.qr(y,x,tau)

ALDqr documentation built on May 30, 2017, 8:26 a.m.