NPMLE for Student t non-centrality parameter mixtures

Description

Kiefer Wolfowitz NPMLE for Student t non-centrality parameter mixtures Model: y_{ig} = mu_{g} + e_{ig}, e_{ig} ~ N(0,sigma_{g}^{2}) x is the vector of t statistics for all groups, which follows t dist if mu_g = 0, and noncentral t dist if mu_g \neq 0, with ncp_{g} = μ_g / σ_{g}. This leads to a mixture of t distribution with ncp as the mixing parameter. df (degree of freedom) is determined by the group size in the simplest case.

Usage

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2
Tncpmix(x, v = 300, u = 300, df = 1, hist = FALSE, weights = NULL,
  ...)

Arguments

x

Data: Sample Observations

v

bin boundaries defaults to equal spacing of length v

u

bin boundaries for histogram binning: defaults to equal spacing

df

Number of degrees of freedom of Student base density

hist

If TRUE then aggregate x to histogram weights

weights

replicate weights for x obervations, should sum to 1

...

optional parameters passed to KWDual to control optimization

Value

An object of class density with components:

x

midpoints of evaluation on the domain of the mixing density

y

estimated function values at the points x of the mixing density

g

estimated function values at the observed points of mixture density

logLik

Log likelihood value at the proposed solution

dy

Bayes rule estimates of location at x

status

Mosek exit code

Author(s)

Roger Koenker

References

Kiefer, J. and J. Wolfowitz Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters Ann. Math. Statist. 27, (1956), 887-906.

See Also

GLmix for Gaussian version

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