NPMLE for Longitudinal Gaussian Means and Variances Model

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Description

A Kiefer-Wolfowitz NPMLE procedure for estimation of a Gaussian model with independent mean and variance components with weighted longitudinal data. This version exploits a Student t decomposition of the likelihood.

Usage

1
WTLVmix(y, id, w, u = 300, v = 300, ...)

Arguments

y

A vector of observations

id

A strata indicator vector indicating grouping of y

w

A vector of weights corresponding to y

u

A vector of bin boundaries for the mean effects

v

A vector of bin boundaries for the variance effects

...

optional parameters to be passed to KWDual to control optimization

Value

A list consisting of the following components:

u

midpoints of the mean bin boundaries

fu

the function values of the mixing density of the means

v

midpoints of the variance bin boundaries

fv

the function values of the mixing density of the variances.

logLik

log likelihood value for mean problem

du

Bayes rule estimate of the mixing density means.

dv

Bayes rule estimate of the mixing density variances.

status

Mosek convergence status

Author(s)

J. Gu and R. Koenker

References

Gu, J. and R. Koenker (2015) Empirical Bayesball Remixed, preprint

See Also

WGLVmix for a more general bivariate mixing distribution version

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