NPMLE for Longitudinal Gaussian Means and Variances Model with Independent Prior

Description

A Kiefer-Wolfowitz NPMLE procedure for estimation of a Gaussian model with independent mean and variance prior components with weighted longitudinal data. This version iterates back and forth from Gamma and Gaussian forms of the likelihood.

Usage

1
WLVmix(y, id, w, u = 300, v = 300, eps = 1e-04, maxit = 2, ...)

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

eps

Convergence tolerance for iterations

maxit

A limit on the number of allowed iterations

...

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

vector of log likelihood values for each iteration

du

Bayes rule estimate of the mixing density means.

dv

Bayes rule estimate of the mixing density variances.

status

Mosek convergence status for each iteration

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 and WTLVmix for an alternative estimator exploiting a Student/Gamma decomposition

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