Article (Scientific journals)
When the score function is the identity function - A tale of characterizations of the normal distribution
Ley, Christophe
2020In Econometrics and Statistics
Peer reviewed
 

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Keywords :
Maximum likelihood characterization; Score function; Skew-symmetric distributions; Stein characterization; Variance bounds
Abstract :
[en] The normal distribution is well-known for several results that it is the only to fulfil. Much less well-known is the fact that many of these characterizations follow from the fact that the derivative of the log-density of the normal distribution is the (negative) identity function. This a priori very simple yet surprising observation allows a deeper understanding of existing characterizations and paves the way for an immediate extension of various seemingly normal-based characterizations to a general density by replacing the (negative) identity function in these results with the derivative of that log-density.
Disciplines :
Mathematics
Author, co-author :
Ley, Christophe ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
External co-authors :
no
Language :
English
Title :
When the score function is the identity function - A tale of characterizations of the normal distribution
Publication date :
19 November 2020
Journal title :
Econometrics and Statistics
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 20 June 2022

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