1\name{sm.ts.pdf} 2\alias{sm.ts.pdf} 3\title{ 4Nonparametric density estimation of stationary time series data 5} 6\description{ 7This function estimates the density function of a time series \code{x}, 8assumed to be stationary. The univariate marginal density is estimated 9in all cases; bivariate densities of pairs of lagged values are estimated 10depending on the parameter \code{lags}. 11} 12\usage{ 13sm.ts.pdf(x, h = hnorm(x), lags, maxlag = 1, ask = TRUE) 14} 15\arguments{ 16\item{x}{ 17a vector containing a time series 18} 19\item{h}{ 20bandwidth 21} 22\item{lags}{ 23 for each value, \code{k} say, in the vector \code{lags} a density 24 estimate is produced 25 of the joint distribution of the pair \code{(x(t-k),x(t))}. 26} 27\item{maxlag}{ 28 if \code{lags} is not given, it is assigned the value \code{1:maxlag} 29 (default=1). 30} 31\item{ask}{ 32 if \code{ask=TRUE}, the program pauses after each plot, until <Enter> 33 is pressed. 34} 35} 36\value{ 37a list of two elements, containing the outcome of the estimation of 38the marginal density and the last bivariate density, as produced by 39\code{\link{sm.density}}. 40} 41\section{Side Effects}{ 42plots are produced on the current graphical device. 43} 44\details{ 45see Section 7.2 of the reference below. 46} 47\references{ 48Bowman, A.W. and Azzalini, A. (1997). \emph{Applied Smoothing Techniques for 49Data Analysis: the Kernel Approach with S-Plus Illustrations.} 50Oxford University Press, Oxford. 51} 52\seealso{ 53\code{\link{sm.density}}, \code{\link{sm.autoregression}} 54} 55\examples{ 56with(geyser, { 57 sm.ts.pdf(geyser$duration, lags=1:2) 58}) 59} 60\keyword{nonparametric} 61\keyword{smooth} 62\keyword{ts} 63% Converted by Sd2Rd version 1.15. 64