rmtdata


Contains large data for 500 observations and 100 variables based
               on standard normal independent random variables. It has 15
               spikes. The number of spikes can be correctly detected using
               the fitting procedure as described in the code.


We implement a collection of techniques for estimating covariance matrices.
Covariance matrices can be built using missing data. Stambaugh Estimation and
FMMC methods can be used to construct such matrices. Covariance matrices can
be built by denoising or shrinking the eigenvalues of a sample covariance
matrix. Such techniques work by exploiting the tools in Random Matrix Theory
to analyse the distribution of eigenvalues. Covariance matrices can also
be built assuming that data has many underlying regimes. Each regime is
allowed to follow a Dynamic Conditional Correlation model. Robust covariance
matrices can be constructed by multivariate cleaning and smoothing of noisy data.

rmtdata: Simulated data for Spiked Covarianve Model

Description

Usage

Arguments