Seite - 330 - in Differential Geometrical Theory of Statistics
Bild der Seite - 330 -
Text der Seite - 330 -
Entropy2016,18, 421
3.DivergencesandGoodness-of-Fit
Theemphasisof thissectionis the importanceof theboundaryof theextendedmultinomialwhen
understandingthe linksbetweeninformationgeometricdivergencesandfamiliesofgoodness-of-fit
statistics. For completeness, a set ofwell-knownresults linking thePower-Divergence familyand
informationgeometry in themanifoldsenseare surveyed inSections3.1–3.3. Theextension to the
extendedmultinomialfamilyisdiscussedinSection3.4,wherewemakeclearhowtheglobalbehaviour
ofdivergences isdominatedbyboundaryeffects. Thiscomplements theusual localanalysis,which
linksdivergenceswith theFisher information, [8]. Perhaps thekeypoint is that, sincecounts in the
datacanbezero, informationgeometricstructuresshouldalsoallowprobabilities tobezero.Hence,
closuresofexponential familiesseemtobethecorrectgeometricobject toworkon.
3.1. ThePower-DivergenceFamily
The results of Section2 concern theboundarybehaviourof two importantmembersof a rich
classofgoodness-of-fit statistics.Animportantunifyingframeworkwhichencompasses theseand
other importantstatisticscanbefoundin[5] (page16)with theso-calledPower-Divergencestatistics.
Thesearedefined, for−∞<λ<∞, by
2NIλ (n
N :π )
:= 2
λ(λ+1) k
∑
i=0 ni [(
ni
Nπi )λ
−1
]
, (11)
with thecasesλ=−1,0beingdefinedbytakingtheappropriate limit togive
lim
λ→−1 2NIλ (n
N :π )
=2 k
∑
i=0 Nπi log(Nπi/ni) , lim
λ→0 2NIλ (n
N :π )
=2 k
∑
i=0 ni log(ni/Nπi) .
Importantspecial casesareshowninTable1 (whosefirstcolumnisdescribedbelowinSection3.3),
andwealsonote thecaseλ=2/3,whichReadandCressie recommend[5] (page79)asareasonably
robust statisticwithaneasily calculable criticalvalue for smallN. Ina sense, it lies “between” the
Pearsonχ2 anddeviancestatistics,whichwecomparedinSection2.
Table1.Special casesof thePower-Divergencestatistics.
α:=1+2λ λ Formula Name
3 1 ∑ki=0 (ni−Nπi)2
Nπi Pearsonχ 2
7/3 2/3 95∑ k
i=0ni [(
ni
Nπi )2
3 −1 ]
Read–Cressie
1 0 2∑ki=0ni log(ni/Nπi) Twice log-likelihood(deviance)
0 −12 4∑ki=0 (√ ni− √
Nπi )2 Freeman–TukeyorHellinger
−1 −1 2∑ki=0Nπi log(Nπi/ni) Twicemodifiedlog-likelihood
−3 −2 ∑ki=0 (ni−Nπi) 2
ni Neymanχ 2
Thispaper isprimarilyconcernedwith thesparsecasewheremanyof theni countsarezero,and
wearealso interested in lettingprobabilities,πi, becomingarbitrarilysmall,orevenzero.
3.2. LiteratureReview
Before we look at this, we briefly review the literature on the geometry of goodness-of-fit
statistics.Agoodsource for thehistoricaldevelopments (in thediscretecontext) canbefoundin[5]
(pages131–153)and [7]. Important examples include theanalysisof contingency tables, log-linear,
330
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