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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tsybakov.R
\name{tsybakov}
\alias{tsybakov}
\alias{Tsybakov}
\title{The Tsybakov mode estimator}
\usage{
tsybakov(
x,
bw = NULL,
a,
alpha = 0.9,
kernel = "triangular",
dmp = TRUE,
par = shorth(x)
)
}
\arguments{
\item{x}{numeric. Vector of observations.}
\item{bw}{numeric. Vector of length \code{length(x)}
giving the sequence of smoothing bandwidths to be used.}
\item{a}{numeric. Vector of length \code{length(x)} used in the
gradient algorithm}
\item{alpha}{numeric. An alternative way of specifying \code{a}. See 'Details'.}
\item{kernel}{character. The kernel to be used. Available kernels are
\code{"biweight"}, \code{"cosine"}, \code{"eddy"},
\code{"epanechnikov"}, \code{"gaussian"}, \code{"optcosine"},
\code{"rectangular"}, \code{"triangular"}, \code{"uniform"}.
See \code{\link[stats]{density}} for more details on some
of these kernels.}
\item{dmp}{logical. If \code{TRUE}, Djeddour et al.
version of the estimate is used.}
\item{par}{numeric. Initial value in the gradient algorithm.
Default value is \code{\link[modeest]{shorth}(x)}.}
}
\value{
A numeric value is returned, the mode estimate.
}
\description{
This mode estimator is based on a gradient-like recursive algorithm,
more adapted for online estimation.
It includes the Mizoguchi-Shimura (1976) mode estimator,
based on the window training procedure.
}
\details{
If \code{bw} or \code{a} is missing, a default
value advised by Djeddour et al (2003) is used:
\code{bw = (1:length(x))^(-1/7)} and \code{a = (1:length(x))^(-alpha)}.
(with \code{alpha = 0.9} if \code{alpha} is missing).
}
\note{
The user may call \code{tsybakov} through
\code{mlv(x, method = "tsybakov", ...)}.
}
\section{Warning}{
The Tsybakov mode estimate as it is presently
computed does not work very well.
The reasons of this inefficiency should be further investigated.
}
\examples{
x <- rbeta(1000, shape1 = 2, shape2 = 5)
## True mode:
betaMode(shape1 = 2, shape2 = 5)
## Estimation:
tsybakov(x, kernel = "triangular")
tsybakov(x, kernel = "gaussian", alpha = 0.99)
mlv(x, method = "tsybakov", kernel = "gaussian", alpha = 0.99)
}
\references{
\itemize{
\item Mizoguchi R. and Shimura M. (1976).
Nonparametric Learning Without a Teacher Based on Mode Estimation.
\emph{IEEE Transactions on Computers}, \bold{C25}(11):1109-1117.
\item Tsybakov A. (1990).
Recursive estimation of the mode of a multivariate distribution.
\emph{Probl. Inf. Transm.}, \bold{26}:31-37.
\item Djeddour K., Mokkadem A. et Pelletier M. (2003).
Sur l'estimation recursive du mode et de la valeur modale d'une densite de
probabilite.
\emph{Technical report 105}.
\item Djeddour K., Mokkadem A. et Pelletier M. (2003).
Application du principe de moyennisation a l'estimation recursive du mode
et de la valeur modale d'une densite de probabilite.
\emph{Technical report 106}.
}
}
\seealso{
\code{\link[modeest]{mlv}} for general mode estimation.
}
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