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
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:
{
the number of poin 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.