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In \cite{Lomb}, Lomb developed a nonlinear regression approach to estimating the frequency of a noisy sinusoid when the measurement times were not equispaced, and a method for correcting the times so that the resulting regression sum of squares appeared very similar to the usual periodogram.

Poster for ICASSP 2016

Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise.

Linear signal estimation based on sample covariance matrices (SCMs) can perform poorly if the training data are limited and the SCMs are ill-conditioned. Diagonal loading (DL) may be used to improve robustness in the face of limited training data. This paper introduces two leave-one-out cross-validation schemes for choosing the DL factor. One scheme repeatedly splits the training data with respect to time, while the other repeatedly splits the out-of-training data with respect to space.