sm.regression.autocor: Nonparametric regression with autocorrelated errors

Description

This function estimates nonparametrically the regression function of y on x when the error terms are serially correlated.

Usage

sm.regression.autocor(x=1:n, y, h.first, minh, maxh, method="direct", ...)

Arguments

vector of the response values

h.first

the smoothing parameter used for the initial smoothing stage.

vector of the covariate values; if unset, it is assumed to be 1:length(y).

minh

the minimum value of the interval where the optimal smoothing parameter is searched for (default is 0.5).

maxh

the maximum value of the interval where the optimal smoothing parameter is searched for (default is 10).

method

character value which specifies the optimality criterium adopted; possible values are "no.cor", "direct" (default), and "indirect".

...

other optional parameters are passed to the sm.options function, through a mechanism which limits their effect only to this call of the function; those relevant for this function are the following:

ngrid

the number of points to be considered in (minh,maxh). Default: ngrid=15.

display

if this is not equal to "none", graphical output is produced on the current graphical device.

Value

a list as returned from sm.regression called with the new value of smoothing parameter, with an additional term $aux added which contains the initial value h.first, the estimated curve using h.first, the autocorrelation function of the residuals from the initial fit, and the residuals.

Side Effects

a new suggested value for h is printed; also, if the parameter display is not equal to "none", graphical output is produced on the current graphical device.

Details

see Section 7.5 of the reference below.

References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.