Estimating the Rank of the Spectral Density Matrix

By Gonzalo Camba-Mendez, George Kapetanios. 2004

19 citations recorded by the source corpus.

Abstract.  The rank of the spectral density matrix conveys relevant information in a variety of statistical modelling scenarios. This note shows how to estimate the rank of the spectral density matrix at any given frequency. The method presented is valid for any hermitian positive definite matrix estimate that has a normal asymptotic distribution with a covariance matrix the rank of which is known.

Subfields: Statistical and Nonlinear Physics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical and Nonlinear Physics

1. Bernhard Schölkopf
2. Mingming Gong
3. Kun Zhang
4. George Kapetanios
5. Manuel Gomez-Rodriguez
6. Jure Leskovec

In Signal Processing

1. Bernhard Schölkopf
2. Kun Zhang
3. Mingming Gong
4. George Kapetanios
5. Dominik Janzing
6. David Lopez-Paz
7. Jan Peters
8. Arthur Gretton

Citations in the corpus

• Multi-Source Domain Adaptation: A Causal View (2015, methodology): “We also varied the number of source domains from 3 to 5, and the rank of Q j is still 3, as confirmed by the test of the rank of Hermitian positive semidefinite matrices (Camba-Mendez and Kapetanios 2005).”
• A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets (2010, background): “Then, it follows a considerable body of work on rank determination using a sequence of tests such as Camba-Mendez et al. (2003) and Camba-Mendez and Kapetanios (2005).”