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2 edition of Conditional extremes and near-extremes found in the catalog.

Conditional extremes and near-extremes

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Published by Massachusetts Institute of Technology, Dept. of Economics in Cambridge, MA .
Written in English


About the Edition

This paper develops a theory of high and low (extremal) quantile regression: the linear models, estimation, and inference. In particular, the models coherently combine the convenient, flexible linearity with the extreme-value-theoretic restrictions on tails and the general heteroscedasticity forms. Within these models, the limit laws for extremal quantile regression statistics are obtained under the rank conditions (experiments) constructed to reflect the extremal or rare nature of tail events. An inference framework is discussed. The results apply to cross-section (and possibly dependent) data. The applications, ranging from the analysis of babies" very low birth weights, (S,s) models, tail analysis in heteroscedastic regression models, outlier-robust inference in auction models, and decision-making under extreme uncertainty, provide the motivation and applications of this theory. Keywords: Quantile regression, extreme value theory, tail analysis, (S,s) models, auctions, price search, Extreme Risk. JEL Classifications: C13, C14, C21, C41, C51, C53, D21, D44, D81.

Edition Notes

StatementVictor Chernozhukov
SeriesWorking paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 01-21, Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 01-21.
ContributionsMassachusetts Institute of Technology. Dept. of Economics
The Physical Object
Pagination41 p. :
Number of Pages41
ID Numbers
Open LibraryOL24639681M
OCLC/WorldCa49829433

Conditional Distributions and Independence Definition Let (X,Y) be a discrete bivariate random vector with joint pmf f(x,y) andmarginal pmfs fX(x) and fY (y).For any x such that P(X = x) = fX(x) > 0, the conditional pmf of Y given that X = x is the function of y denoted by f(y|x) and defined by f(y|x) = P(Y = y|X = x) = f(x,y) fX(x) For any y such that P(Y = y) = fY (y) > 0, the File Size: 81KB. CONDITIONAL EXTREMES FROM HEAVY-TAILED DISTRIBUTIONS: AN APPLICATION TO THE ESTIMATION OF EXTREME RAINFALL RETURN LEVELS Laurent Gardes and St´ephane Girard⋆ Team Mistis, INRIA Rhoˆne-Alpes & Laboratoire Jean Kuntzmann , avenue de l’Europe, Montbonnot, Saint-Ismier Cedex, France. ⋆ Corresponding author, @ extreme wind speeds, regardless of’their direction, in regions not subjected to hurricanes. Using the Generalized Pareto Distribution (GPD) approach and the Conditional Mean Exceedance (CME) estimation method, we analyze yearto year sets of largest annual speeds and sets drawn from 48 year to year records of maximum daily wind. 4 Extreme Steps People Take to Avoid Disapproval you adapted to your caretakers’ conditional approval of you. As the title of Terry Cole Whittaker’s self-help book emphatically.


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Conditional extremes and near-extremes by Victor Chernozhukov Download PDF EPUB FB2

CONDITIONALEXTREMESANDNEAR-EXTREMES VictorChernozhukov^ omics,MIT,EF, Cambridge,MA, email:[email protected] Version:May10, FirstVersion:October. Download Citation | Conditional Extremes and Near-Extremes | This paper develops a theory of high and low (extremal) quantile regression: the linear models, estimation, and : Victor Chernozhukov.

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