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wmonfromx |
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Find monotone decreasing Empirical Bayes Weight from Data |
DESCRIPTION
Given a vector of data, find the marginal maximum likelihood choice of weight sequence subject to the constraints that the weights are monotone decreasing
USAGE
w = wmonfromx(x.prior,a)
REQUIRED ARGUMENTS
| x | a vector of data |
OPTIONAL ARGUMENTS
| prior |
specification of the prior to be used; can be 'cauchy' or 'laplace' |
| a | If the Laplace prior is used, a is the scale factor. If the Cauchy prior is used, a is ignored. |
VALUE
The vector of
estimated weights is returned.
BACKGROUND
The weights is found by marginal maximum likelihood. The search is over weights corresponding to thresholds in the range [0, sqrt(2*log(n))], where n=length(x). An iterated least squares monotone regression algorithm is used to maximize the log likelihood. The weighted least squares monotone regression routine isotone is used.
SEE ALSO