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Entropy2016,18, 421
(a) (+1)−affine parameters
ξ1
ξ2 (b) (−1)−affine parameters
π1
0.0 0.2 0.4 0.6 0.8 1.0
Figure1. Level sets ofK(π0,π), forfixedπ0 = (16, 2
6, 3
6) in: (a) thenaturalparameters, and (b) the
meanparameters.
(a) (+1)−affine parameters
ξ1
ξ2 (b) (−1)−affine parameters
π1
0.0 0.2 0.4 0.6 0.8 1.0
Figure2. Level setsofK∗(π0,π), forfixedπ0=(16, 2
6, 3
6) in: (a) thenaturalparameters, and (b) the
meanparameters.
4. SimulationStudies
In this section,weundertakesimulationstudies tonumericallyexplorewhathasbeendiscussed
above. Separatesub-sectionsaddress threegeneral topics—focusingononeparticular instanceofeach,
as follows:
1. The transition as (N,k) varies between discrete and continuous features of the sampling
distributions of goodness-of-fit statistics—focusing on the behaviour of the deviance at the
uniformdiscretedistribution;
2. ThecomparativebehaviourofarangeofPower-Divergencestatistics—focusingontherelative
stabilityof their samplingdistributionsnear theboundary;
3. The lackofuniformity—across theparameterspace—of thefinitesampleadequacyofstandard
asymptotic samplingdistributions, focusingontesting independence in2×2contingencytables.
333
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