| hcvc.fun {Ake} | R Documentation |
Cross-validation function for bandwidth selection for continuous data
Description
The (S3) generic function hcvc.fun computes the
cross-validation bandwidth selector.
Usage
hcvc.fun(Vec,...)
## Default S3 method:
hcvc.fun(Vec, bw = NULL, type_data, ker, a0 = 0, a1 = 1, ...)
Arguments
Vec |
The data sample from which the estimate is to be computed. |
bw |
The sequence of bandwidths where to compute the cross-validation. Default value is NULL. |
type_data |
The sample data type. |
ker |
The associated kernel. |
a0 |
The left bound of the extended beta. Default value is 0. |
a1 |
The right bound of the extended beta.Default value is 1. |
... |
Further arguments. |
Details
hcvc.fun implements the choice of the bandwidth h using the cross-validation approach
of a kernel density estimator.
Value
Returns a list containing:
hcv |
value of bandwidth parameter. |
CV |
the values of cross-validation function. |
seq_h |
the sequence of bandwidths where the cross validation is computed. |
Author(s)
W. E. Wansouwé, S. M. Somé and C. C. Kokonendji
References
Chen, S. X. (1999). Beta kernels estimators for density functions, Computational Statistics and Data Analysis 31, 131 - 145.
Chen, S. X. (2000). Gamma kernels estimators for density functions, Annals of the Institute of Statistical Mathematics 52, 471 - 480.
Libengué, F.G. (2013). Méthode Non-Paramétrique par Noyaux Associés Mixtes et Applications, Ph.D. Thesis Manuscript (in French) to Université de Franche-Comté, Besançon, France and Université de Ouagadougou, Burkina Faso, June 2013, LMB no. 14334, Besançon.
Igarashi, G. and Kakizawa, Y. (2015). Bias correction for some asymmetric kernel estimators, Journal of Statistical Planning and Inference 159, 37 - 63.
Examples
V=rgamma(100,1.5,2.6)
## Not run:
hcvc.fun(V,NULL,"continuous","GA")
## End(Not run)