surg

<p>Intestinal surgery data involving 844 cancer patients. The data consists of
pairs (\(n_i\), \(s_i\)) where \(n_i\) is the number of satellites removed
and \(s_i\) is the number of satellites found to be malignant.</p>

data

Empirical Bayes methods for learning prior distributions from data.
An unknown prior distribution (g) has yielded (unobservable) parameters, each of
which produces a data point from a parametric exponential family (f). The goal
is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
Bayes methods. Details and examples are in the paper by Narasimhan and Efron
(2020, <doi:10.18637/jss.v094.i11>).

Balasubramanian Narasimhan

deconvolveR

Empirical Bayes Estimation Strategies

Bradley Efron

surg function

Intestinal surgery data involving 844 cancer patients. The data consists of
pairs (\(n_i\), \(s_i\)) where \(n_i\) is the number of satellites removed
and \(s_i\) is the number of satellites found to be malignant. — surg

Intestinal surgery data involving 844 cancer patients. The data consists of
pairs (\(n_i\), \(s_i\)) where \(n_i\) is the number of satellites removed
and \(s_i\) is the number of satellites found to be malignant.

surg: Intestinal surgery data involving 844 cancer patients. The data consists of pairs (\(n_i\), \(s_i\)) where \(n_i\) is the number of satellites removed and \(s_i\) is the number of satellites found to be malignant.

Description

Usage

Arguments

References