BwQuant (version 0.1.0)

llqr: Fitting a local linear quantile regression model

Description

Function that estimates the quantile regression function using a local linear kernel smoother.

Usage

llqr(x, y, tau, t, h)

Arguments

x

numeric vector of x data.

y

numeric vector of y data. This must be the same length as x.

tau

the quantile order where the regression function is to be estimated. It must be a number strictly between 0 and 1.

t

the values of x at which the quantile regression model is to be estimated.

h

the bandwidth parameter.

Value

A list with the following components:

x.values

the given points at which the evaluation occurs.

y.values

the estimated values of the quantile regression function at the given x.values.

References

Fan, J., Hu, T. C. and Truong, Y. K. (1994). Robust nonparametric function estimation. Scandinavian Journal of Statistics, 21, 433-446.

Yu, K. and Jones, M. C. (1998). Local linear quantile regression. Journal of the American Statistical Association, 93, 228-237.

See Also

The argument h with the bandwidth parameter can be fixed to some arbitrary value or chosen by one of the procedures implemented in the functions bwCV, bwPI, bwRT or bwYJ.

Examples

# NOT RUN {
set.seed(1234)
x=runif(100)
y=10*(x^4+x^2-x)+rexp(100)
tau=0.25
h=bwPI(x,y,tau)
t=seq(0,1,length=101)
m=llqr(x,y,tau,t,h)
plot(x,y)
lines(m$x.values,m$y.values)
# }