estimate_noise: Estimating Noise

In IrisTeng/CVEK: Cross-Validated Kernel Ensemble

Description

An implementation of Gaussian processes for estimating noise.

Usage

estimate_noise(Y, lambda_hat, beta_hat, alpha_hat, K_hat)

Arguments

Y	(vector of length n) Reponses of the dataframe.
lambda_hat	(numeric) The selected tuning parameter based on the estimated ensemble kernel matrix.
beta_hat	(numeric) Estimated bias of the model.
alpha_hat	(vector of length n) Estimated coefficients of the estimated ensemble kernel matrix.
K_hat	(matrix, n*n) Estimated ensemble kernel matrix.

Value

sigma2_hat	(numeric) The estimated noise of the fixed effects.
SSE	(numeric) The estimated noise of the fixed effects.
A	(matrix) The estimated noise of the fixed effects.

Author(s)

Wenying Deng

References

Jeremiah Zhe Liu and Brent Coull. Robust Hypothesis Test for Nonlinear Effect with Gaus- sian Processes. October 2017.

Examples

sigma2_hat <- estimate_noise(Y, lam, beta0, alpha0, K_gpr)

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