Seite - 332 - in Differential Geometrical Theory of Statistics
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Entropy2016,18, 421
representation for the Poisson is given inTable 2. Thedivergence parameter—inwhichwehave
convexity—isshownforeachλ, as is theso-calledpotential function,whichgenerates thecomplete
informationgeometry for thesemodels.
Table2.Power-Divergence in thePoissonmodelwithmeanμ,whereλ∗=1−λ.
λ α DivergenceDλ(μ1,μ2) DivergenceParameterξ Potential
−1 −1 μ1−μ2−μ2(log(μ1)− log(μ2)) ξ= log(μ) exp(ξ)
0 1 μ2−μ1−μ1(log(μ2)− log(μ1)) ξ=μ ξ log(ξ)−ξ
λ =0,−1 α =±1 (
λ∗μ1−λ∗μ2−μ2 ((
μ1
μ2 )λ∗−1))
λ∗(1−λ∗) ξ= 1
λ∗μ λ∗ (λ∗ξ)1/λ ∗
1−λ∗
3.4. ExtendedMultinomialCase
In thispaper,weare focusingon the class of log–linearmodelswhere themultinomial is the
underlyingclassofdistributions; that is,weconditiononthesamplesize,N,beingfixedintheproduct
Poissonspace. Inparticular,wefocusonextendedmultinomials,which includes theclosureof the
multinomials, sowehaveaboundary.Dueto theconditioning(which inducescurvature),only the
caseswhereλ=0,−1remainBregmandivergences,butall arestilldivergences in thesenseofbeing
Csiszár f-divergences [36,37].
Theclosureofanexponentialfamily(e.g., [11,38–40]),anditsapplicationinthetheoryoflog–linear
modelshasbeenexplored in [12,13,41,42].Thekeyhere isunderstanding the limitingbehaviour in
the natural—α = 1 in the sense of [8]—parameter space. This can be done by considering the
polar dual [43], or, alternatively, thedirections of recession—[12] or [42]. Theboundarypolytope
determineskeystatisticalpropertiesof themodel, includingthebehaviourof thesamplingdistribution
of (functionsof) theMLEandtheshapeof level setsofdivergence functions.
Figures 1 and 2 show level sets of the α = ±1 Power-Divergences in the (+1)-affine and
(−1)-affine parameters (Panels (a) and (b), respectively) for the k = 2 extended multinomial
model. The boundary polytope in this case is a simple triangle “at infinity”, and the shape of
this is strongly reflected in the behaviour of the level sets. In Figure 1,we show—in the
simplex{
(π0,π1,π2)|∑2i=0πi=1,πi≥0 }
—the level sets of the α =−1divergence,which, in theCsiszár
f-divergence form, is
K(π0,π) := 2
∑
i=0 log (
π0i
πi )
π0i .
ThefiguresshowhowinPanel (a), thedirectionsof recessiondominate theshapeof level sets, andin
Panel (b) thedualsof thesedirections (i.e., theverticesof thesimplex)eachhavedifferentmaximal
behaviour. The lackofconvexityof the level sets inPanel (a)correspondsto the fact that thenatural
parametersarenot theaffinedivergenceparameters for thisdivergence, sowedonotexpectconvex
behaviour. InPanel (b),wedogetnon-convex level sets, asexpected.
Figure2showsthesamestory,but this timefor thedualdivergence,
K∗(π,π0) :=K(π0,π).
Now, theaffinedivergenceparametersareshowninPanel (a), thenaturalparameters.Wesee that
in the limit theshapeof thedivergence isconvergingto thatof thepolarof theboundarypolytope.
Ingeneral, localbehaviour isquadratic,butboundarybehaviour ispolygonal.
332
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