kernel: character. The kernel to be used. For available kernels see densityfun in package statip.
abc: logical. If FALSE (the default), the kernel density estimate is maximised using optim.
tolerance: numeric. Desired accuracy in the optimize function.
...: If abc = FALSE, further arguments to be passed to optim.
Returns
parzen returns a numeric value, the mode estimate. If abc = TRUE, the x value maximizing the density estimate is returned. Otherwise, the optim
method is used to perform maximization, and the attributes: 'value', 'counts', 'convergence' and 'message', coming from the optim method, are added to the result.
Details
If kernel = "uniform", the naive mode estimate is returned.
Note
The user may call parzen through mlv(x, method = "kernel", ...) or mlv(x, method = "parzen", ...).
Presently, parzen is quite slow.
Examples
# Unimodal distribution x <- rlnorm(10000, meanlog =3.4, sdlog =0.2)## True mode lnormMode(meanlog =3.4, sdlog =0.2)## Estimate of the mode mlv(x, method ="kernel", kernel ="gaussian", bw =0.3, par = shorth(x))
References
Parzen E. (1962). On estimation of a probability density function and mode. Ann. Math. Stat., 33 (3):1065--1076.
Konakov V.D. (1973). On the asymptotic normality of the mode of multidimensional distributions. Theory Probab. Appl., 18 :794-803.
Eddy W.F. (1980). Optimum kernel estimators of the mode. Ann. Statist., 8 (4):870-882.
Eddy W.F. (1982). The Asymptotic Distributions of Kernel Estimators of the Mode. Z. Wahrsch. Verw. Gebiete, 59 :279-290.
Romano J.P. (1988). On weak convergence and optimality of kernel density estimates of the mode. Ann. Statist., 16 (2):629-647.
Abraham C., Biau G. and Cadre B. (2003). Simple Estimation of the Mode of a Multivariate Density. Canad. J. Statist., 31 (1):23-34.
Abraham C., Biau G. and Cadre B. (2004). On the Asymptotic Properties of a Simple Estimate of the Mode. ESAIM Probab. Stat., 8 :1-11.