This function estimates nonparametrically the regression function
of y on x when the error terms are serially correlated.
sm.regression.autocor(x = 1:n, y, h.first, minh, maxh, method = "direct", ...)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.
vector of the response values
the smoothing parameter used for the initial smoothing stage.
vector of the covariate values; if unset, it is assumed to
be 1:length(y).
the minimum value of the interval where the optimal smoothing parameter is searched for (default is 0.5).
the maximum value of the interval where the optimal smoothing parameter is searched for (default is 10).
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.
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.
see Section 7.5 of the reference below.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.regression, sm.autoregression