Hi Armin,
Laplace-Normal random variables may be generated as the sum of a Normal
rv and the difference of two exponential rvs. See
Reed, W.J. and Jorgensen, M.A. (2004) The Double Pareto-Lognormal
distribution – A new parametric model for size distributions.
Communications in Statistics B: Theory and Methods, 33(8), 1733-1753.
Perhaps you can get around your problem using this representation or
alternatively reproduce the problem with one of the component rvs.
Cheers,
Murray Jorgensen
Hi,
I have to draw samples from an asymmetric-Laplace-Normal distribution:
f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) +
(2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and
y=(y_1, ..., y_n), x=(x_1, ..., x_n)
-- sorry for this huge formula --
A WinBUGS Gibbs sampler and the HI package arms sampler were used with the
same initial data for all parameters. I compared the mean from both the
Gibbs sample and the arms sample for several y and x. Surprisingly, both
means always differed by the same constant.
Shouldn't the sample means be equal? What could be the reason for the
constant difference? (burnin and sample size variation didn't change this)
Thanks in advance
Armin
--
Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: [EMAIL PROTECTED] Fax 7 838 4155
Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 1395 862
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