Description Usage Arguments Details Value Author(s) References See Also
Given a vector of data, find the marginal maximum likelihood choice of weight sequence subject to the constraints that the weights are monotone decreasing.
1 | wmonfromx(xd, prior = "laplace", a = 0.5, tol = 1e-08, maxits = 20)
|
xd |
A vector of data. |
prior |
Specification of the prior to be used; can be
|
a |
Scale parameter in prior if |
tol |
Absolute tolerance to within which estimates are calculated. |
maxits |
Maximum number of weighted least squares iterations within the calculation. |
The weights is found by marginal maximum likelihood. The search is over weights corresponding to thresholds in the range [0, √{2 \log n}], where n is the length of the data vector.
An iterated least squares monotone regression algorithm is used to
maximize the log likelihood. The weighted least squares monotone
regression routine isotone
is used.
To turn the weights into thresholds, use the routine
tfromw
; to process the data with these thresholds, use the
routine threshld
.
The vector of estimated weights is returned.
Bernard Silverman
See ebayesthresh
and
http://www.bernardsilverman.com
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