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Extreme M-quantiles as risk measures: from L1 to Lp optimization (2018)
Journal Article
Daouia, A., Girard, S., & Stupfler, G. (2019). Extreme M-quantiles as risk measures: from L1 to Lp optimization. Bernoulli, 25(1), 264-309

The class of quantiles lies at the heart of extreme-value theory and is one of the basic tools in risk management. The alternative family of expectiles is based on squared rather than absolute error loss minimization. It has recently been receiving a... Read More about Extreme M-quantiles as risk measures: from L1 to Lp optimization.

Extremiles: A New Perspective on Asymmetric Least Squares (2018)
Journal Article
Daouia, A., Gijbels, I., & Stupfler, G. (2019). Extremiles: A New Perspective on Asymmetric Least Squares. Journal of the American Statistical Association, 114(527), 1366-1381. https://doi.org/10.1080/01621459.2018.1498348

Quantiles and expectiles of a distribution are found to be useful descriptors of its tail in the same way as the median and mean are related to its central behavior. This paper considers a valuable alternative class to expectiles, called extremiles,... Read More about Extremiles: A New Perspective on Asymmetric Least Squares.

On the study of extremes with dependent random right-censoring (2018)
Journal Article
Stupfler, G. (2019). On the study of extremes with dependent random right-censoring. Extremes, 22(1), 97–129. https://doi.org/10.1007/s10687-018-0328-6

The study of extremes in missing data frameworks is a recent developing field. In particular, the randomly right-censored case has been receiving a fair amount of attention in the last decade. All studies on this topic, however, essentially work unde... Read More about On the study of extremes with dependent random right-censoring.