tuning_AICc: Calculating Tuning Parameters Using AICc

Description Usage Arguments Details Value Author(s) References

Description

Calculate tuning parameters based on AICc.

Usage

1
tuning_AICc(Y, K_mat, lambda)

Arguments

Y

(vector of length n) Reponses of the dataframe.

K_mat

(matrix, n*n) Estimated ensemble kernel matrix.

lambda

(numeric) A numeric string specifying the range of noise to be chosen. The lower limit of lambda must be above 0.

Details

Akaike Information Criteria

λ_{AICc}=\underset{λ \in Λ}{argmin}\Big\{log\; y^{\star T}(I-A_λ)^2y^\star+\frac{2[tr(A_λ)+2]}{n-tr(A_λ)-3}\Big\}

Value

lambda0

(numeric) The estimated tuning parameter.

Author(s)

Wenying Deng

References

Philip S. Boonstra, Bhramar Mukherjee, and Jeremy M. G. Taylor. A Small-Sample Choice of the Tuning Parameter in Ridge Regression. July 2015.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Series in Statistics. Springer- Verlag, New York, 2 edition, 2009.

Hirotogu Akaike. Information Theory and an Extension of the Maximum Likelihood Princi- ple. In Selected Papers of Hirotugu Akaike, Springer Series in Statistics, pages 199–213. Springer, New York, NY, 1998.

Clifford M. Hurvich and Chih-Ling Tsai. Regression and time series model selection in small samples. June 1989.

Hurvich Clifford M., Simonoff Jeffrey S., and Tsai Chih-Ling. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. January 2002.


IrisTeng/CVEK documentation built on May 31, 2019, 4:50 p.m.