poetEst {cvCovEst}

POET Estimator

Description

poetEst() implements the Principal Orthogonal complEment Thresholding (POET) estimator, a nonparametric, unobserved-factor-based estimator of the covariance matrix (Fan et al. 2013). The estimator is defined as the sum of the sample covariance matrix' rank-k approximation and its post-thresholding principal orthogonal complement. The hard thresholding function is used here, though others could be used instead.

Usage

poetEst(dat, k, lambda)

Arguments

dat

A numeric data.frame, matrix, or similar object.

k

An integer indicating the number of unobserved latent factors. Empirical evidence suggests that the POET estimator is robust to overestimation of this hyperparameter (Fan et al. 2013). In practice, it is therefore preferable to use larger values.

lambda

A non-negative numeric defining the amount of thresholding applied to each element of sample covariance matrix's orthogonal complement.

Value

A matrix corresponding to the estimate of the covariance matrix.

References

Fan J, Liao Y, Mincheva M (2013). “Large covariance estimation by thresholding principal orthogonal complements.” Journal of the Royal Statistical Society. Series B (Statistical Methodology), 75(4), 603–680. ISSN 13697412, 14679868, https://www.jstor.org/stable/24772450.

Examples

poetEst(dat = mtcars, k = 2L, lambda = 0.1)