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Root n consistent extremile regression and its supervised and semi-supervised learning

Autor: Jiang, Rong, Yu, Keming

Extremile (Daouia, Gijbels and Stupfler,2019) is a novel and coherent measure of risk, determined by weighted expectations rather than tail probabilities. It finds application in risk management, and, in contrast to quantiles, it fulfills the axioms

Externí odkaz: http://arxiv.org/abs/2310.07107

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Bayesian Fractional Polynomial Approach to Quantile Regression and Variable Selection with Application in the Analysis of Blood Pressure among US Adults

Autor: Soomro, Sanna, Yu, Keming

Hypertension is a highly prevalent chronic medical condition and a strong risk factor for cardiovascular disease (CVD), as it accounts for more than $45\%$ of CVD. The relation between blood pressure (BP) and its risk factors cannot be explored clear

Externí odkaz: http://arxiv.org/abs/2307.12137

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A New Bayesian Huberised Regularisation and Beyond

Autor: Soomro, Sanna, Yu, Keming, Yu, Yan

Robust regression has attracted a great amount of attention in the literature recently, particularly for taking asymmetricity into account simultaneously and for high-dimensional analysis. However, the majority of research on the topics falls in freq

Externí odkaz: http://arxiv.org/abs/2307.12123

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Modile as a conservative tail risk measurer: the solution of an optimisation problem with 0-1 loss function

Autor: Yu, Keming, Jiang, Rong, Ng, Chi Tim

Quantiles and expectiles, which are two important concepts and tools in tail risk measurements, can be regarded as an extension of median and mean, respectively. Both of these tail risk measurers can actually be embedded in a common framework of $L_p

Externí odkaz: http://arxiv.org/abs/2306.12117

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