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Differential Geometrical Theory of Statistics
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Entropy2016,18, 396 computationalcomplexity.Unfortunately, eigenfunctionsof theLaplacianoperatorareknownonDn butnotoncompactsub-domains. This iswhythestudyof thismethodhasnotbeendeepened. 3.3. Kernels LetK :R+→R+beamapwhichverifies the followingproperties: (i) ∫ RdK(||x||)dx=1; (ii) ∫ Rd xK(||x||)dx=0; (iii) K(x>1)=0; (iv) sup(K(x))=K(0). Let p ∈ Dn. Generally, given apoint p on aRiemannianmanifold, expp defines an injective applicationonlyonaneighborhoodof0. On theSiegel space, expp is injectiveon thewhole space. Whenthe tangentspaceTpDn isendowedwith the local scalarproduct, ||u||= d(p,expp(u)), where ||.|| is theEuclideandistanceassociatedwiththelocalscalarproductandd(., .) is theRiemannian distance. The correspondingLebesguemeasureonTpDn is noted Lebp. Let exp∗p(Lebp)denote the push-forwardmeasureofLeppby expp. The functionθpdefinedby: θp : q → θp(q)= dvoldexp∗p(Lebp) (q) (6) is thedensityof theRiemannianmeasureonDnwithrespect to theLebesguemeasureLebp after the identificationofDn andTpDn inducedby expp (seeFigure1). Figure1.M isaRiemannianmanifold,andTxM is its tangentspaceatx. Theexponentialapplication inducesavolumechangeθxbetweenTxMandM. GivenKandapositiveradius r, theestimatorof f proposedby[1] isdefinedby: fˆk= 1 k∑i 1 rn 1 θxi(x) K ( d(x,xi) r ) . (7) Thecorrective factor θxi(x) −1 isnecessary since thekernelKoriginally integrates toonewith respect to theLebesguemeasureandnotwithrespect to theRiemannianmeasure. It canbenoticed that thisestimator is theusualkernelestimator in thecaseofEuclideanspace.Whenthecurvatureof thespace isnegative,which is thecaseof theSiegel space, thedistributionplacedovereachsample xihasxi as intrinsicmean. Thefollowingtheoremprovidesconvergencerateof theestimator. It isa directadaptationofTheorem3.1of [1]. Theorem1. Let (M,g) be aRiemannianmanifold of dimensionn andμ itsRiemannianvolumemeasure. LetXbearandomvariable taking itsvalues inacompact subsetCof (M,g). Let0< r≤ rinj,where rinj is the infimumof the injectivity radiusonC.Assumethe lawofXhasa twicedifferentiabledensity f with respect to 352
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Differential Geometrical Theory of Statistics
Titel
Differential Geometrical Theory of Statistics
Autoren
Frédéric Barbaresco
Frank Nielsen
Herausgeber
MDPI
Ort
Basel
Datum
2017
Sprache
englisch
Lizenz
CC BY-NC-ND 4.0
ISBN
978-3-03842-425-3
Abmessungen
17.0 x 24.4 cm
Seiten
476
Schlagwörter
Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
Kategorien
Naturwissenschaften Physik
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Differential Geometrical Theory of Statistics