# risk_stein: Stein Risk function from Pang et al. (2009). In sparsediscrim: Sparse and Regularized Discriminant Analysis

## Description

This function finds the value for alpha \in [0,1] that empirically minimizes the average risk under a Stein loss function, which is given on page 1023 of Pang et al. (2009).

## Usage

 1 risk_stein(N, K, var_feature, num_alphas = 101, t = -1) 

## Arguments

 N the sample size. K the number of classes. var_feature a vector of the sample variances for each dimension. num_alphas The number of values used to find the optimal amount of shrinkage. t a constant specified by the user that indicates the exponent to use with the variance estimator. By default, t = -1 as in Pang et al. See the paper for more details.

## Value

list with

• alpha: the alpha that minimizes the average risk under a Stein loss function. If the minimum is not unique, we randomly select an alpha from the minimizers.

• risk: the minimum average risk attained.

## References

Pang, H., Tong, T., & Zhao, H. (2009). "Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data," Biometrics, 65, 4, 1021-1029. http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2009.01200.x/abstract

sparsediscrim documentation built on Aug. 14, 2017, 5:10 p.m.