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This function estimates the density function of a time series x,
assumed to be stationary. The univariate marginal density is estimated
in all cases; bivariate densities of pairs of lagged values are estimated
depending on the parameter lags.
sm.ts.pdf(x, h = hnorm(x), lags, maxlag = 1, ask = TRUE)a list of two elements, containing the outcome of the estimation of
the marginal density and the last bivariate density, as produced by
sm.density.
a vector containing a time series
bandwidth
for each value, k say, in the vector lags a density
estimate is produced
of the joint distribution of the pair (x(t-k),x(t)).
if lags is not given, it is assigned the value 1:maxlag
(default=1).
if ask=TRUE, the program pauses after each plot, until <Enter>
is pressed.
plots are produced on the current graphical device.
see Section 7.2 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.density, sm.autoregression
with(geyser, {
sm.ts.pdf(geyser$duration, lags=1:2)
})
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