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Entropic Projections and Dominating Points

Published online by Cambridge University Press:  22 December 2010

Christian Léonard*
Affiliation:
Modal-X, Université Paris Ouest, Bât. G, 200 av. de la République, 92000 Nanterre, France; leonard@u-paris10.fr
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Abstract

Entropic projections and dominating points are solutions to convexminimization problems related to conditional laws of largenumbers. They appear in many areas of applied mathematics such asstatistical physics, information theory, mathematical statistics, ill-posed inverse problems or large deviation theory. By means of convex conjugateduality and functional analysis, criteria are derived for theexistence of entropic projections, generalized entropicprojections and dominating points. Representations of thegeneralized entropic projections are obtained. It is shown thatthey are the “measure component" of the solutions to someextended entropy minimization problem. This approach leads to newresults and offers a unifying point of view. It also permits toextend previous results on the subject by removing unnecessarytopological restrictions. As a by-product, new proofs of alreadyknown results are provided.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2010

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References

R. Azencott, Grandes déviations et applications, in École d'Eté de Probabilités de Saint-Flour VIII (1978).
Borwein, J.M. and Lewis, A.S., Strong rotundity and optimization. SIAM J. Optim. 1 (1994) 146158. CrossRef
Boucher, C., Ellis, R.S. and Turkington, B., Spatializing random measures: doubly indexed processes and the large deviation principle. Ann. Probab. 27 (1999) 297324.
Csiszár, I., I-divergence geometry of probability distributions and minimization problems. Ann. Probab. 3 (1975) 146158. CrossRef
Csiszár, I., Sanov property, generalized I-projection and a conditional limit theorem. Ann. Probab. 12 (1984) 768793. CrossRef
Csiszár, I., Generalized projections for non-negative functions. Acta Math. Hungar. 68 (1995) 161185. CrossRef
Csiszár, I., Gamboa, F. and Gassiat, E., MEM pixel correlated solutions for generalized moment and interpolation problems. IEEE Trans. Inform. Theory 45 (1999) 22532270. CrossRef
Dacunha-Castelle, D. and Gamboa, F., Maximum d'entropie et problème des moments. Ann. Inst. H. Poincaré. Probab. Statist. 26 (1990) 567596.
A. Dembo and O. Zeitouni, Large Deviations Techniques and Applications. Second edition. Appl. Math. 38. Springer-Verlag (1998).
Einmahl, U. and Kuelbs, J., Dominating points and large deviations for random vectors. Probab. Theory Relat. Fields 105 (1996) 529543. CrossRef
Ellis, R.S., Gough, J. and Puli, J.V., The large deviations principle for measures with random weights. Rev. Math. Phys. 5 (1993) 659692. CrossRef
Gamboa, F. and Gassiat, E., Bayesian methods and maximum entropy for ill-posed inverse problems. Ann. Statist. 25 (1997) 328350.
H. Gzyl, The Method of Maximum Entropy. World Scientific (1994).
Kuelbs, J., Large deviation probabilities and dominating points for open convex sets: nonlogarithmic behavior. Ann. Probab. 28 (2000) 12591279. CrossRef
Léonard, C., Large deviations for Poisson random measures and processes with independent increments. Stoch. Proc. Appl. 85 (2000) 93121. CrossRef
Léonard, C., Convex minimization problems with weak constraint qualifications. J. Convex Anal. 17 (2010) 321348.
Léonard, C., Minimization of energy functionals applied to some inverse problems. J. Appl. Math. Optim. 44 (2001) 273297.
Léonard, C., Minimizers of energy functionals under not very integrable constraints. J. Convex Anal. 10 (2003) 6388.
Léonard, C., Minimization of entropy functionals. J. Math. Anal. Appl. 346 (2008) 183204. CrossRef
Léonard, C. and Najim, J., An extension of Sanov's theorem: application to the Gibbs conditioning principle. Bernoulli 8 (2002) 721743.
Najim, J., Cramér, A type theorem for weighted random variables. Electron. J. Probab. 7 (2002) 132. CrossRef
Dominating, P. Ney points and the asymptotics of large deviations for random walks on Rd . Ann. Probab. 11 (1983) 158167.
Convexity, P. Ney and large deviations. Ann. Probab. 12 (1984) 903906.
M.M. Rao and Z.D. Ren, Theory of Orlicz spaces, Pure Appl. Math. 146. Marcel Dekker, Inc. (1991).
Rockafellar, R.T., Integrals which are convex functionals. Pacific J. Math. 24 (1968) 525539. CrossRef
R.T. Rockafellar, Conjugate Duality and Optimization, volume 16 of Regional Conf. Series in Applied Mathematics. SIAM, Philadelphia (1974).
R.T. Rockafellar and R. Wets, Variational Analysis, in Grundlehren der Mathematischen Wissenschaften, volume 317. Springer (1998).
Schied, A., Cramér's condition and Sanov's theorem. Statist. Probab. Lett. 39 (1998) 5560. CrossRef
C.R. Smith, G.J. Erickson and P.O. Neudorfer (Eds.). Maximum Entropy and Bayesian Methods, Proc. of 11th Int. Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis, Seattle, 1991. Kluwer.
D.W. Stroock and O. Zeitouni, Microcanonical distributions, Gibbs states and the equivalence of ensembles, in Festchrift in Honour of F. Spitzer, edited by R. Durrett and H. Kesten. Birkhaüser (1991) 399–424.