Statistical consequences of fat tails (Record no. 29684)

000 -LEADER
fixed length control field 02028 a2200193 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241120162523.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781544508054
040 ## - CATALOGING SOURCE
Original cataloging agency ICTS-TIFR
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA273.6
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Nassim Nicholas Taleb
245 ## - TITLE STATEMENT
Title Statistical consequences of fat tails
Remainder of title : real world preasymptotics, epistemology, and applications : papers and commentary
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. STEM Academic Press,
Date of publication, distribution, etc. [c2020]
Place of publication, distribution, etc. U.S.A.:
300 ## - Physical Description
Pages: 441 p.
490 ## - SERIES STATEMENT
Series statement The Technical Incerto Collection
520 ## - SUMMARY, ETC.
Summary, etc. The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible. Switching from thin tailed to fat tailed distributions requires more than “changing the color of the dress.” Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the “laws of the medium numbers”–which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on “naïve empiricism,” but can be sometimes be estimated via parametric methods. - The “empirical distribution” is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many “biases” found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Shelving location Date acquired Inventory number Full call number Accession No. Koha item type
        Mathematics ICTS Rack No 5 07/03/2023 IN-174 dt.05/04/2023 QA273.6 02634 Book