Page - 247 - in Differential Geometrical Theory of Statistics
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Entropy2016,18, 383
Proposition1. Let sāRr>0. ForξāPāV, onehas:
Īās(Js(ξ))ā1=( r
ā
k=1 sskk )Ī“ās(ξ). (45)
Proof. Weprove the statementby inductionon the rank. When r= 1, theequality (45) isveriļ¬ed
directly. Indeed, the left-handsideof (45) is computedas ( s1ξ11) s1 = ss11 ξ ās1
11 .
When r>1,assumethat (45)holds forasystemofrank rā1.Wededuce from(31), (33), (43), (44)
andthe inductionhypothesis that (45)holds forξāPāVā©Z0V. Therefore, (45)holds forallξāPāV by
(30), (34)and(42).
Ingeneral, foranon-zerofunction f, thefunction 1fā¦(ālog f)ā1 iscalledthemultiplicativeLegendre
transformof f. Thanks toTheorem5andProposition 1,we see that themultiplicative Legendre
transformofĪās(x) isequal toĪ“ās(āξ)onāPāV uptoconstantmultiple.Asacorollary,wearriveat
the followingresult.
Theorem6. TheFenchelāLegendre transformof the convex function logĪās onPV is equal to the function
logĪ“ās(āξ)ofξāāPāuptoconstantaddition.
6.ApplicationtoStatisticsandOptimization
Take sāRr forwhich sk> qk/2 (k=1,. . . ,r).Wedeļ¬neameasureĻVs onPāV by:
ĻVs (dξ) :=Cā1V ĪV(s) ā1Ī“Vs (ξ)ĻV(ξ)dξ (ξāPāV). (46)
Theorem4states that: ā«
PāV eā(x|ξ)ĻVs (dξ)=ĪVās(x) (xāPV).
Then, we obtain the natural exponential family generated by ĻVs , that is a family {μVs,x}xāPV of
probabilitymeasuresonPāV givenby:
μVs,x(dξ) :=ĪVs (x)eā(x|ξ)ĻVs (dξ).
In particular, when s = (n1α,n2α, . . . ,nrα) for sufļ¬ciently large α, we have μVs,x(dξ) =
(detx)αeā(x|ξ)ĻVs (dξ).WecallμVs,x theWishartdistributionsonPāV ingeneral.
Fromasampleξ0āPāV, letusestimate theparameterxāPV insuchawaythat the likelihood
functionĪVs (x)eā(x|ξ) attains itsmaximumat theestimatorx0. Then,wehavethe likelihoodequation
ξ0=IVs (x0),whereasTheorem5givesauniquesolutionbyx0=JVs (ξ0).
Thesameargument leadsus to the followingresult in semideļ¬niteprogramming. Foraļ¬xed
ξ0āPāV andα>0,auniquesolutionx0 of theminimizationproblemof (x|ξ0)āαlogdetx subject to
xāPV=ZVā©Pn isgivenbyx0=JVs (ξ0),where s=(n1α, . . . ,nrα).Note thatJVs isarationalmap
becauseΓVs isaproductofpowersof rational functions.
7. SpecialCases
7.1.MatrixRealizationofHomogeneousCones
Letusassumethat thesystemV={Vlk}1ā¤k<lā¤r satisļ¬esnotonly theconditions (V1)and(V2),
butalso the following:
(V3)AāVlk,BāVkjāABāVlj (1⤠j< k< l⤠r).
247
Differential Geometrical Theory of Statistics
- Title
- Differential Geometrical Theory of Statistics
- Authors
- FrƩdƩric 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