This function estimates nonparametrically the regression function
of y
on x
when the error terms are serially correlated.
1  sm.regression.autocor(x = 1:n, y, h.first, minh, maxh, method = "direct", ...)

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 
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 
... 
other optional parameters are passed to the 
see Section 7.5 of the reference below.
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.
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.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with SPlus Illustrations. Oxford University Press, Oxford.
sm.regression
, sm.autoregression
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