An Alternative Proposal: Acoustic Techniques to Asses Detection and Monitoring of Toxic Algal Blooms

Blanc, Silvia; Mosto, Patricia; Milou, Marta. E. de; Benitez, C.

doi:10.4067/s0717-65382004000200009

Cited by 2 publications

(2 citation statements)

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“…Therefore, in the last decade, ultrasound techniques have been developed to obtain information about these organisms. See for example, Blanc et al (2004), Bok et al (2010) and Blanc et al (2017).…”

Section: Application To Real Datamentioning

confidence: 99%

Robust Model-Based Clustering

et al. 2022

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We propose a class of Fisher-consistent robust estimators for mixture models. These estimators are then used to build a robust model-based clustering procedure. We study in detail the case of multivariate Gaussian mixtures and propose an algorithm, similar to the EM algorithm, to compute the proposed estimators and build the robust clusters. An extensive Monte Carlo simulation study shows that our proposal outperforms other robust and non robust, state of the art, model-based clustering procedures. We apply our proposal to a real data set and show that again it outperforms alternative procedures.

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Section: Application To Real Datamentioning

confidence: 99%

Robust Model-Based Clustering

et al. 2022

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“…The acoustic monitoring of phytoplankton is a potentially useful technique for estimating the abundance of these organisms in real time. Therefore, in the last decade ultrasound Blanc et al (2004), Bok et al (2010) and Blanc et al (2017).…”

Section: Application To Real Datamentioning

confidence: 99%

Robust Model-Based Clustering

González¹,

Maronna²,

Yohai³

et al. 2021

Preprint

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We propose a new class of robust and Fisher-consistent estimators for mixture models.These estimators can be used to construct robust model-based clustering procedures. We study in detail the case of multivariate normal mixtures and propose a procedure that uses S estimators of multivariate location and scatter. We develop an algorithm to compute the estimators and to build the clusters which is quite similar to the EM algorithm. An extensive Monte Carlo simulation study shows that our proposal compares favorably with other robust and non robust model-based clustering procedures. We apply ours and alternative procedures to a real data set and again find that the best results are obtained using our proposal.

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An Alternative Proposal: Acoustic Techniques to Asses Detection and Monitoring of Toxic Algal Blooms

Cited by 2 publications

References 0 publications

Robust Model-Based Clustering

Robust Model-Based Clustering

Robust Model-Based Clustering

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