Efficient spectral analysis in the missing data case using sparse ML methods

• Given their wide applicability, several sparse high-resolution spectral estimation techniques and their implementation have been examined in the recent literature. In this work, we further the topic by examining a computationally efficient implementation of the recent SMLA algorithms in the missing data case. The work is an extension of our implementation for the uniformly sampled case, and offers a notable computational gain as compared to the alternative implementations in the missing data case.

Subject headings

NATURVETENSKAP  -- Matematik (hsv//swe)

NATURAL SCIENCES  -- Mathematics (hsv//eng)

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Keyword

fast algorithms

Sparse Maximum Likelihood methods

Spectral estimation theory and methods

Computation theory

Signal processing

Spectrum analysis

Computational gains

Computationally efficient

High resolution

Maximum likelihood methods

Missing data

Spectral Estimation

Spectral estimation techniques

Maximum likelihood estimation

Spectral estimation theory and methods

Sparse Maximum Likelihood methods

fast algorithms

Publication and Content Type

ref (subject category)

kon (subject category)