Page - 103 - in Differential Geometrical Theory of Statistics
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Entropy2016,18, 386
l’esprit à cette opération, est certainementd’unusageplus étenduquecelui où tout est soumisà l’évidence;
parceque les occasionsde sedéterminer surdesvraisemblancesouprobabilités, sontplus fréquentesquecelles
qui exigentqu’onprocèdepardémonstrations: pourquoinedirions–nouspasque souvent elles tiennentaussi
àdesobjetsbeaucoupplus importants?
—JosephdeMaistreinL’EspitdeFinesse[221]
Le cadavre qui s’acoutre se méconnait et imaginant l’éternité s’en approrie l’illusion . . . C’est pourquoi
j’abandonnerai ces frusques et jetant lemasquedemes jours, je fuirai le tempsoù,deconcert avec lesautres,
jem’éreinte àme trahir.
—EmileCioraninPrécisdedecomposition[222]
Conflictsof Interest:Theauthordeclaresnoconflictof interest.
AppendixA. Clairaut(-Legendre)EquationofMauriceFréchetAssociatedto“Distinguished
Functions”asFundamentalEquationofInformationGeometry
BeforeRao [223,224], in 1943,Maurice Fréchet [141]wrote a seminal paper introducingwhat
was thencalled theCramer-Raobound. Thispapercontains in factmuchmore that this important
discovery. Inparticular,MauriceFréchet introducesmoregeneralnotionsrelative to“distinguished
functions”,densitieswithestimator reachingthebound,definedwitha function, solutionofClairaut’s
equation. Thesolutions“envelopeof theClairaut’sequation”areequivalent tostandardLegendre
transformwithout convexity constraintsbutonly smoothnessassumption. ThisFréchet’s analysis
canberevisitedonthebasisof Jean-LouisKoszul’sworksasaseminal foundationof“information
geometry”.
WewilluseMauriceFréchetnotations, toconsider theestimator:
T=H(X1,...,Xn) (A1)
andtherandomvariable
A(X)= ∂logpθ(X)
∂θ (A2)
thatareassociatedto:
U=∑
i A(Xi) (A3)
Thenormalizingconstraint +∞
−∞ pθ(x)dx=1 implies that: +∞
−∞ ... +∞
−∞ ∏
i pθ(xi)dxi=1
Ifweconsider thederivative if this lastexpressionwithrespect toθ, then
+∞
−∞ ... +∞
−∞ [
∑
i A(xi) ]
∏
i pθ(xi)dxi=0gives :Eθ [U]=0 (A4)
Similarly, ifweassumethatEθ [T]= θ, then +∞
−∞ ... +∞
−∞ H(x1,...,xn)∏
i pθ(xi)dxi= θ, andweobtain
byderivationwithrespect toθ:
E [(T−θ)U]=1 (A5)
ButasE [T]= θandE [U]=0,we immediatelydeduce that:
E [(T−E [T])(U−E [U])]=1 (A6)
FromSchwarz inequality,wecandevelopthe followingrelations:
[E(ZT)]2≤E[Z2]E[T2]
1≤E [
(T−E [T])2 ]
E [
(U−E [U])2 ]
=(σTσU) 2 (A7)
103
Differential Geometrical Theory of Statistics
- Title
- Differential Geometrical Theory of Statistics
- Authors
- Frédéric Barbaresco
- Frank Nielsen
- Editor
- MDPI
- Location
- Basel
- Date
- 2017
- Language
- English
- License
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03842-425-3
- Size
- 17.0 x 24.4 cm
- Pages
- 476
- Keywords
- Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
- Categories
- Naturwissenschaften Physik