Given a vector of data, find the marginal maximum likelihood choice of weight sequence subject to the constraints that the weights are monotone decreasing.
wmonfromx(xd, prior = "laplace", a = 0.5, tol = 1e-08, maxits = 20)A vector of data.
Specification of the prior to be used; can be
cauchy or laplace.
Scale parameter in prior if prior="laplace". Ignored
if prior="cauchy".
Absolute tolerance to within which estimates are calculated.
Maximum number of weighted least squares iterations within the calculation.
The vector of estimated weights is returned.
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 \(n\) 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.