fitHeavyTail: Mean and Covariance Matrix Estimation under Heavy Tails

Robust estimation methods for the mean vector and covariance matrix from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian, Cauchy, and Student's t. Additionally, a factor model structure can be specified for the covariance matrix. The package is based on the papers: Y. Sun, P. Babu, and D. P. Palomar (2014). Regularized Tyler’s Scatter Estimator: Existence, Uniqueness, and Algorithms. IEEE Trans. on Signal Processing, vol. 62, no. 19, pp. 5143-5156. <doi:10.1109/TSP.2014.2348944>. Y. Sun, P. Babu, and D. P. Palomar (2015). Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions. IEEE Trans. on Signal Processing, vol. 63, no. 12, pp. 3096-3109. <doi:10.1109/SAM.2014.6882356>. Chuanhai Liu and Donald B. Rubin (1995). ML estimation of the t-distribution using EM and its extensions, ECM and ECME. Statistica Sinica (5), pp. 19-39. <https:www3.stat.sinica.edu.tw/statistica/oldpdf/A5n12.pdf>. R. Zhou, J. Liu, S. Kumar, and D. P. Palomar (2015). Robust factor analysis parameter estimation. Lecture Notes in Computer Science (LNCS). <arXiv:1909.12530>.

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