sm.regression.autocor: Nonparametric regression with autocorrelated errors

In sm: Smoothing Methods for Nonparametric Regression and Density Estimation

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

y	vector of the response values
h.first	the smoothing parameter used for the initial smoothing stage.
x	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, display; see the documentation of sm.options for their description.

Details

see Section 7.5 of the reference below.

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.

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.

See Also

sm.regression, sm.autoregression

sm documentation built on May 29, 2024, 2:28 a.m.