Výsledky vyhledávání - "FERNÁNDEZ, MIGUEL ÁNGEL"

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Regression trees for nonparametric diagnostics of sequential positivity violations in longitudinal causal inference

Autor: Chatton, Arthur, Schomaker, Michael, Luque-Fernandez, Miguel-Angel, Platt, Robert W., Schnitzer, Mireille E.

Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it usually relies on multiple parametric prop

Externí odkaz: http://arxiv.org/abs/2412.10245

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Style-agnostic evaluation of ASR using multiple reference transcripts

Autor: McNamara, Quinten, Fernández, Miguel Ángel del Río, Bhandari, Nishchal, Ratajczak, Martin, Chen, Danny, Miller, Corey, Jetté, Migüel

Word error rate (WER) as a metric has a variety of limitations that have plagued the field of speech recognition. Evaluation datasets suffer from varying style, formality, and inherent ambiguity of the transcription task. In this work, we attempt to

Externí odkaz: http://arxiv.org/abs/2412.07937

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Reverb: Open-Source ASR and Diarization from Rev

Autor: Bhandari, Nishchal, Chen, Danny, Fernández, Miguel Ángel del Río, Delworth, Natalie, Fox, Jennifer Drexler, Jetté, Migüel, McNamara, Quinten, Miller, Corey, Novotný, Ondřej, Profant, Ján, Qin, Nan, Ratajczak, Martin, Robichaud, Jean-Philippe

Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these rel

Externí odkaz: http://arxiv.org/abs/2410.03930

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Performance of Cross-Validated Targeted Maximum Likelihood Estimation

Autor: Smith, Matthew J., Phillips, Rachael V., Maringe, Camille, Luque-Fernandez, Miguel Angel

Background: Advanced methods for causal inference, such as targeted maximum likelihood estimation (TMLE), require certain conditions for statistical inference. However, in situations where there is not differentiability due to data sparsity or near-p

Externí odkaz: http://arxiv.org/abs/2409.11265

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Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

Autor: Smith, Matthew J., Phillips, Rachael V., Luque-Fernandez, Miguel Angel, Maringe, Camille

The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient and robust strategy for estimation and inference of

Externí odkaz: http://arxiv.org/abs/2303.07329

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The Delta-Method and Influence Function in Medical Statistics: a Reproducible Tutorial

Autor: Zepeda-Tello, Rodrigo, Schomaker, Michael, Maringe, Camille, Smith, Matthew J., Belot, Aurelien, Rachet, Bernard, Schnitzer, Mireille E., Luque-Fernandez, Miguel Angel

Approximate statistical inference via determination of the asymptotic distribution of a statistic is routinely used for inference in applied medical statistics (e.g. to estimate the standard error of the marginal or conditional risk ratio). One metho

Externí odkaz: http://arxiv.org/abs/2206.15310

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Akademický článek

The asset-backing risk of stablecoin trading: The case of Tether

Autor: Fernández Francisco Javier Jorcano, Fernández Miguel Ángel Echarte, Alonso Sergio Luis Náñez

Publikováno v: Economics and Business Review, Vol 10, Iss 1, Pp 57-80 (2024)

This article aims to analyse the asset-backing risk of stablecoins, focusing on international accounting standards, classification criteria, and auditing standards and using Tether as a case study. It examines Tether’s issuance, backing, controls,

Externí odkaz: https://doaj.org/article/71551db9bd4d4fb59c6c13489c2e837a

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