fitHeavyTail - Mean and Covariance Matrix Estimation under Heavy Tails

Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).

Last updated 2 years ago

cauchycovariance-estimationcovariance-matrixheavy-tailed-distributionsoutliersrobust-estimationstudent-ttyler

6.25 score 21 stars 1 dependents 28 scripts 676 downloads

intradayModel - Modeling and Forecasting Financial Intraday Signals

Models, analyzes, and forecasts financial intraday signals. This package currently supports a univariate state-space model for intraday trading volume provided by Chen (2016) <doi:10.2139/ssrn.3101695>.

Last updated 2 years ago

5.55 score 13 stars 27 scripts 576 downloads

finbipartite - Learning Bipartite Graphs: Heavy Tails and Multiple Components

Learning bipartite and k-component bipartite graphs from financial datasets. This package contains implementations of the algorithms described in the paper: Cardoso JVM, Ying J, and Palomar DP (2022). <https://openreview.net/pdf?id=WNSyF9qZaMd> "Learning bipartite graphs: heavy tails and multiple components, Advances in Neural Informations Processing Systems" (NeurIPS).

Last updated 2 years ago

3.22 score 3 stars 11 scripts 189 downloads