BDGLmix: Efron Bayesian Deconvolution Estimator for Gaussian Mixtures

In REBayes: Empirical Bayes Estimation and Inference

Efron Bayesian Deconvolution Estimator for Gaussian Mixtures

Description

Efron (2016, 2019) penalized logspline density estimator for Gaussian mixture model g-modeling. Returns an object of class GLmix to facilitate prediction compatible with Kiefer-Wolfowitz GLmix estimation. In particular percentile confidence intervals can be constructed based on posterior quantiles. Assumes homoscedastic standard Gaussian noise, for the moment.

Usage

BDGLmix(y, T = 300, sigma = 1, df = 5, c0 = 0.1)

Arguments

y	Data: Sample Observations
T	Undata: Grid Values defaults equal spacing of with T bins, when T is a scalar
sigma	scale parameter of the Gaussian noise, may take vector value of length(y)
df	degrees of freedom of the natural spline basis
c0	penalty parameter for the Euclidean norm penalty.

Value

An object of class GLmix, density with components:

x	points of evaluation on the domain of the density
y	estimated function values at these points of the mixing density
sigma	returns a sigma = 1 for compatibility with GLmix

Author(s)

Adapted from a similar implementation in the R package deconvolveR of Narasimhan and Efron.

References

Efron, B. (2016) Empirical Bayes deconvolution estimates, Biometrika, 103, 1–20, Efron, B. (2019) Bayes, Oracle Bayes and Empirical Bayes, Statistical Science, 34, 177-201.

REBayes documentation built on Aug. 19, 2023, 5:10 p.m.