The minimum weighted covariance determinant estimator for high-dimensional data

Kalina, Jan; Tichavský, Jan

doi:10.1007/s11634-021-00471-6

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…Furthermore, Boudt et al (2020) generalizes the MCD to high-dimensional cases as the minimum regularized covariance determinant (MRCD). Other variants include the orthogonalized Gnanadesikan-Kettenring (Maronna and Zamar, 2002), the minimum (regularized) weighted covariance determinant (Roelant et al, 2009;Kalina and Tichavskỳ, 2021), and kernel MRCD for non-elliptical data (Schreurs et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Commercialization of state-owned broadcast networks in China

Yan¹

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Visual contents, such as illustrations and images, play a big role in product manual understanding. Existing Product Manual Question Answering (PMQA) datasets tend to ignore visual contents and only retain textual parts. In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. For each question, MPMQA requires the model not only to process multimodal contents but also to provide multimodal answers. To support MPMQA, a large-scale dataset PM209 is constructed with human annotations, which contains 209 product manuals from 27 well-known consumer electronic brands. Human annotations include 6 types of semantic regions for manual contents and 22,021 pairs of question and answer. Especially, each answer consists of a textual sentence and related visual regions from manuals. Taking into account the length of product manuals and the fact that a question is always related to a small number of pages, MP-MQA can be naturally split into two subtasks: retrieving most related pages and then generating multimodal answers. We further propose a unified model that can perform these two subtasks all together and achieve comparable performance with multiple task-specific models. The PM209 dataset is available at https://github.com/AIM3-RUC/MPMQA.

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Section: Introductionmentioning

confidence: 99%

Commercialization of state-owned broadcast networks in China

Yan¹

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Regularized linear discriminant analysis based on generalized capped $$l_{2,q}$$-norm

Ren

Guo

et al. 2022

Ann Oper Res

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Consistency factor for the MCD estimator at the Student-t distribution

Barabesi,

Cerioli,

García-Escudero

et al. 2023

Stat Comput

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It is well known that trimmed estimators of multivariate scatter, such as the Minimum Covariance Determinant (MCD) estimator, are inconsistent unless an appropriate factor is applied to them in order to take the effect of trimming into account. This factor is widely recommended and applied when uncontaminated data are assumed to come from a multivariate normal model. We address the problem of computing a consistency factor for the MCD estimator in a heavy-tail scenario, when uncontaminated data come from a multivariate Student-t distribution. We derive a remarkably simple computational formula for the appropriate factor and show that it reduces to an even simpler analytic expression in the bivariate case. Exploiting our formula, we then develop a robust Monte Carlo procedure for estimating the usually unknown number of degrees of freedom of the assumed and possibly contaminated multivariate Student-t model, which is a necessary ingredient for obtaining the required consistency factor. Finally, we provide substantial simulation evidence about the proposed procedure and apply it to data from image processing and financial markets.

show abstract

The minimum weighted covariance determinant estimator for high-dimensional data

Cited by 7 publications

References 39 publications

Commercialization of state-owned broadcast networks in China

Commercialization of state-owned broadcast networks in China

Regularized linear discriminant analysis based on generalized capped $$l_{2,q}$$-norm

Consistency factor for the MCD estimator at the Student-t distribution

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