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- Babu, Prabhu, et al.
(författare)
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A combined linear programming-maximum likelihood approach to radial velocity data analysis for extrasolar planet detection
- 2011
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Ingår i: ICASSP2011, the 36th International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic. - 9781457705397 ; , s. 4352-4355
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Konferensbidrag (refereegranskat)abstract
- In this paper we introduce a new technique for estimating the parameters of the Keplerian model commonly used in radial velocity data analysis for extrasolar planet detection. The unknown parameters in the Keplerian model, namely eccentricity e, orbital frequency f, periastron passage time T, longitude of periastron., and radial velocity amplitude K are estimated by a new approach named SPICE (a semi-parametric iterative covariance-based estimation technique). SPICE enjoys global convergence, does not require selection of any hyperparameters, and is computationally efficient (indeed computing the SPICE estimates boils down to solving a numerically efficient linear program (LP)). The parameter estimates obtained from SPICE are then refined by means of a relaxation-based maximum likelihood algorithm (RELAX) and the significance of the resultant estimates is determined by a generalized likelihood ratio test (GLRT). A real-life radial velocity data set of the star HD 9446 is analyzed and the results obtained are compared with those reported in the literature.
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- Babu, Prabhu, et al.
(författare)
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Connection between SPICE and Square-Root LASSO for sparse parameter estimation
- 2014
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 95, s. 10-14
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Tidskriftsartikel (refereegranskat)abstract
- In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator.
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| 9. |
- Babu, Prabhu, et al.
(författare)
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Multiple Hypothesis Testing-Based Cepstrum Thresholding for Nonparametric Spectral Estimation
- 2022
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Ingår i: IEEE Signal Processing Letters. - : IEEE. - 1070-9908 .- 1558-2361. ; 29, s. 2367-2371
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Tidskriftsartikel (refereegranskat)abstract
- In this letter we revisit the problem of smoothed nonparametric spectral estimation via cepstrum thresholding. We formulate the problem of cepstrum thresholding as a multiple hypothesis testing problem and use the false discovery rate (FDR) and familywise error rate (FER) procedures to threshold the cepstral coefficients. We compare the FDR and FER approaches with a previously proposed individual hypothesis testing approach and show that the cepstrum thresholding based on FDR and FER can yield spectral estimates with lower mean square error (MSE).
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| 10. |
- Babu, Prabhu, et al.
(författare)
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Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation
- 2023
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 213
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of model selection for high-dimensional sparse linear regression models. We pose the model selection problem as a multiple-hypothesis testing problem and employ the methods of false discovery rate (FDR) and familywise error rate (FER) to solve it. We also present the reformulation of the FDR/FER-based approaches as criterion-based model selection rules and establish their relation to the extended Bayesian Information Criterion (EBIC), which is a state-of-the-art high-dimensional model selection rule. We use numerical simulations to show that the proposed FDR/FER method is well suited for high-dimensional model selection and performs better than EBIC.
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