Download Asymptotics: particles, processes, and inverse problems: by Eric A. Cator, Cor Kraaikamp, Hendrik P. Lopuhaa, Jon A. PDF
By Eric A. Cator, Cor Kraaikamp, Hendrik P. Lopuhaa, Jon A. Wellner, Geurt Jongbloed
Cator E.A., et al. (eds.) Asymptotics.. debris, tactics and inverse difficulties (Inst.Math.Stat., 2007)(ISBN 0940600714)-o
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Extra resources for Asymptotics: particles, processes, and inverse problems: festschrift for Piet Groeneboom
In Nonparametric Techniques in Statistical Inference (M. L. ). Proceedings of the First International Symposium on Nonparametric Techniques held at Indiana University, June 1969 174–176. Cambridge University Press, London.  Millar, P. W. (1979). Asymptotic minimax theorems for the sample distribution function. Z. Wahrsch. Verw. Gebiete 48 233–252. MR0537670 A Kiefer–Wolfowitz theorem 31  Niculescu, C. P. -E. (2006). Convex Functions and Their Applications. Springer, New York. MR2178902 ¨rnberger, G.
A second marshall inequality in convex estimation. Statist. and Probab. Lett. To appear.  Balabdaoui, F. and Wellner, J. A. (2004). Estimation of a k-monotone density, part 1: characterizations, consistency, and minimax lower bounds. Technical report, Department of Statistics, University of Washington.  Balabdaoui, F. and Wellner, J. A. (2004). Estimation of a k-monotone density, part 4: limit distribution theory and the spline connection. Technical report, Department of Statistics, University of Washington.
Fr AMS 2000 subject classiﬁcations: Primary 62M30, 62G05; secondary 62G10, 41A45, 41A46. Keywords and phrases: adaptive estimation, aggregation, intensity estimation, model selection, Poisson processes, robust tests. 32 Model selection for Poisson processes 33 µ(Ai ) and this property characterizes a Poisson process. We shall denote by Qµ the distribution of a Poisson process with mean measure µ on X . 2) φ(Xi ) = exp X i=1 If µ, ν ∈ Q+ (X ) and µ ν, then Qµ [φ(x) − 1] dµ(x) . Qν and N dµ dQµ (Xi ), (X1 , .