tsybakov {modeest} | R Documentation |
This mode estimator is based on a gradient-like recursive algorithm. It includes Mizoguchi-Shimura (1976) mode estimator, based on the window training procedure.
tsybakov(x, bw = NULL, a, alpha = 0.9, kernel = "triangular", djeddour = TRUE, par = shorth(x))
x |
numeric. Vector of observations. |
bw |
numeric. Vector of length length(x) giving the sequence of smoothing bandwidths to be used. |
a |
numeric. Vector of length length(x) used in the gradient algorithm. |
alpha |
numeric. An alternative way of specifying a . See 'Details'. |
kernel |
character. The kernel to be used. Available kernels are
"biweight" , "cosine" , "eddy" , "epanechnikov" ,
"gaussian" , "optcosine" , "rectangular" , "triangular" , "uniform" .
See density.default for more details on some of these kernels. |
djeddour |
logical. If TRUE , Djeddour et al. version of the estimate is used. |
par |
numeric. Initial value in the gradient algorithm.
Default value is shorth(x) . |
If bw
is missing, then bw = (1:length(x))^(-1/7)
, which is the default value advised by Djeddour et al (2003).
If a
is missing, then a = (1:length(x))^(-alpha)
(with alpha = 0.9
is alpha
is missing), which is the default value advised by Djeddour et al (2003).
A numeric value is returned, the mode estimate.
The Tsybakov mode estimate as it is presently computed does not work very well. The reasons of this inefficiency are under investigation.
The user should preferentially call tsybakov
through
mlv(x, method = "tsybakov", ...)
.
This returns an object of class mlv
.
Paul Poncet paulponcet@yahoo.fr
mlv
for general mode estimation
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) M <- mlv(x, method = "tsybakov", kernel = "gaussian", alpha = 0.99) print(M) plot(M)