Draws samples from a smoothed log-concave maximum likelihood
estimate. The estimate should be specified in the form of an object of
class "LogConcDEAD", the result of a call to mlelcd,
and a positive definite matrix.
rslcd(n=1, lcd, A=hatA(lcd), method=c("Independent","MH"))A numeric matrix with n rows, each row corresponding to a point
in \(R^d\) drawn from the distribution with density defined by lcd and A.
A scalar integer indicating the number of samples required
Object of class "LogConcDEAD" (typically output from
mlelcd)
A positive definite matrix that determines the degree of smoothing, typically
taken as the output of hatA(lcd)
Indicator of the method used to draw samples, either via independent rejection sampling (default choice) or via Metropolis-Hastings
Yining Chen
Madeleine Cule
Robert Gramacy
Richard Samworth
This function by default uses a simple rejection sampling scheme to draw independent random samples from a smoothed log-concave maximum likelihood estimator. One can also use the Metropolis-Hastings option to draw (dependent) samples with a higher acceptance rate.
For examples, see mlelcd.
Chen, Y. and Samworth, R. J. (2013) Smoothed log-concave maximum likelihood estimation with applications Statist. Sinica, 23, 1373-1398. https://arxiv.org/abs/1102.1191v4
Cule, M. L., Samworth, R. J., and Stewart, M. I. (2010) Maximum likelihood estimation of a multi-dimensional log-concave density J. Roy. Statist. Soc., Ser. B. (with discussion), 72, 545-600.
Gopal, V. and Casella, G. (2010) Discussion of Maximum likelihood estimation of a log-concave density by Cule, Samworth and Stewart J. Roy. Statist. Soc., Ser. B., 72, 580-582.
mlelcd, rlcd, hatA