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Entropy2016,18, 421 0.0 0.2 0.4 0.6 0.8 1.0 N = 50, nominal level = 0.1 πc 0.00 0.05 0.10 0.15 0.20 0.0 0.2 0.4 0.6 0.8 1.0 N = 50, nominal level = 0.05 πc 0.00 0.02 0.04 0.06 0.08 0.10 Figure8.Heatmapof theactual levelof the test forN=50atnominal levels0.1and0.05; thestandard rule-of-thumb(whereexpectedcountsaregreater than5)applies inside theclosedblackcurvedregion. 5.Discussion Thispaperhas illustrated thekey importanceofworkingwith theboundaryof theclosureof exponential familieswhenstudyinggoodness-of-ïŹt testing in thehighdimensional, lowsamplesize context. Someof thiswork isnew(Section2),whilesomeuses thestructureofextendedexponential families toaddinsight tostandardresults in the literature (Section3). The last section,Section4,uses simulationstudies tostart toexploreopenquestions in thisarea. Oneopenquestion—relatedto theresultsofTheorems1and2—is tosee ifauniïŹedtheory, forall valuesofα, andover largeclassesofextendedexponential families, canbedeveloped. Acknowledgments:Theauthorswould like to thanktheEPSRCfor thesupportofgrantnumberEP/L010429/1. GermainVanBeverwouldalso like to thankFRS-FNRSfor its support throughthegrantFC84444.Wewouldalso like to thankthereferees forveryhelpfulcomments. AuthorContributions: All fourauthorsmadecritical contributions to thepaper. R.S.madekeycontribution to,especially,Section4. P.M.andF.C.providedtheoverall structureandkeycontentdetailsof thepaper.G.V.B. providedinvaluablesuggestions throughout. ConïŹ‚ictsof Interest:TheauthorsdeclarenoconïŹ‚ictsof interest. AppendixA. ProofofTheorem1 Westartbynotinganimportant recurrencerelationwhichwillbeexploited in thecomputations below.BydeïŹnition, forany t :=(ti)∈Rk+1,n=(ni)hasmomentgeneratingfunction M(t;N) :=E{exp(tTn)}=[m(t)]N withm(t)=∑ki=0ai and ai= ai(ti)=πie ti. Putting fN,i(t;r) :=N(r) [m(t)] N−r ari (0≀ r≀N), where N(r) := NPr= { 1 if r=0 N(N−1)...(N−(r−1)) if r∈{1,...,N} , wehave M(t;N)= fN,i(t;0) (0≀ i≀ k) (A1) andtherecurrencerelation: ∂fN,i(t;r) ∂ti = fN,i(t;r+1)+rfN,i(t;r) (0≀ i≀ k; 0≀ r<N) . (A2) 337
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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
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Differential Geometrical Theory of Statistics