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Entropy2016,18, 98 Proposition3states theconvergenceof themaximumlikelihoodestimate YˆN to the truevalueof theparameter Y¯. It isbasedonLemma1,whichstates that theparameter Y¯ is theRiemannianmedian of thedistributionL(Y¯,σ) in thesenseofdefinitionEquation(12). Proposition2 (MLEandmedian). Themaximumlikelihoodestimateof theparameter Y¯ is theRiemannian medianYˆN ofY1, . . . ,YN.Moreover, themaximumlikelihoodestimateof theparameterσ is the solution σˆN of: σ2× d dσ logζm(σ)= 1 N N ∑ n=1 d(Y¯,Yn) (20) Both YˆN and σˆN exist andareunique foranyrealizationof the samplesY1, . . . ,YN. ProofofProposition2. The log-likelihoodfunction,of theparameters Y¯andσ, canbewrittenas: N ∑ n=1 log p(Yn|Y¯,σ) = N ∑ n=1 log ( 1 ζm(σ) e− d(Y¯,Yn) σ ) = −N logζm(σ)− 1 σ N ∑ n=1 d(Y¯,Yn) Asthefirst terminthe lastexpressiondoesnotcontain Y¯, argmaxY¯ N ∑ n=1 log p(Yn|Y¯,σ)=argminY¯ N ∑ n=1 d(Y¯,Yn) Thequantityontheright isexactly YˆN byEquation(11). Thisprovesthefirstclaim.Now,consider the function: F(η)=−N log(ζm(−1 η ))+η N ∑ n=1 d(YˆN,Yn), η< −1 σm This function isstrictlyconcave, since it is the logarithmof themomentgeneratingfunctionofa positivemeasure.Note that limη→−1σm F(η)=−∞, andadmit foramoment that limη→−∞F(η)=−∞. By the strict concavityofF, there exists aunique ηˆN< −1σm (which is themaximumofF), such that F′(ηˆN)= 0. It follows that σˆN = −1ηˆN lies in (0,σm)andsatisfiesEquation (20). Theuniquenessof σˆN isaconsequenceof theuniquenessof ηˆN. Thus, theproof iscomplete.Now, it remains tocheckthat limη→−∞F(η)=−∞or just limσ→+∞ 1σ log(ζm(1σ))=0.Clearly: ∏ i<j sinh (|ri−rj| 2 ) ≤AmeBm|r| whereAm andBm are twoconstantsonlydependingonm. Usingthis, it followsthat: 1 σ log(ζm( 1 σ ))≤ 1 σ log(cmAm)+ 1 σ log (∫ Rm exp((−σ+Bm)|r|)dr1 · · ·drm ) (21) However, forsomeconstantCmonlydependingonm, ∫ Rm exp((−σ+Bm)|r|)dr1 · · ·drm=Cm ∫ ∞ 0 exp((−σ+Bm)u)um−1du ≤ (m−1)!Cm ∫ ∞ 0 exp((−σ+Bm+1)u)du= (m−1)!Cm σ−Bm−1 CombiningthisboundandEquation(21)yields limσ→+∞ 1σ log(ζm( 1 σ))=0. 371
zurück zum  Buch Differential Geometrical Theory of Statistics"
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
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Differential Geometrical Theory of Statistics