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Uniform limit theorems for wavelet density estimators
https://www.statslab.cam.ac.uk/~nickl/Site/__files/AOP447.pdf22 Jul 2009: Donsker classes F. Our proofs will in fact show ‖P Wn Pn‖F = oP (1/. -
Adaptive estimation of a distribution function and its density in…
https://www.statslab.cam.ac.uk/~nickl/Site/__files/BEJ239.pdf19 Nov 2010: Proof. Given ε > 0, apply Proposition 4 below with λ = ε so that ‖F Sn Fn‖ = oP (1/. -
Probab. Theory Relat. Fields (2008) 141:333–387DOI…
https://www.statslab.cam.ac.uk/~nickl/Site/__files/ptrf08.pdf11 Sep 2008: n sup. f F. f (x)pn(x)d x. f (x)p0(x)d x. = OP(1), (3). ... supf F. |(Pn P)(µ̄n f f )| = oP(1/. n). The last two estimates prove (9) since F is P-Donsker. -
Global uniform risk bounds for wavelet deconvolution estimators
https://www.statslab.cam.ac.uk/~nickl/Site/__files/AOS836.pdf17 Feb 2011: The Annals of Statistics2011, Vol. 39, No. 1, 201–231DOI: 10.1214/10-AOS836 Institute of Mathematical Statistics, 2011. GLOBAL UNIFORM RISK BOUNDS FOR WAVELETDECONVOLUTION ESTIMATORS. BY KARIM LOUNICI AND RICHARD NICKL. University of Cambridge. We -
State space collapse and diffusion approximation for a network…
https://www.statslab.cam.ac.uk/~frank/PAPERS/AAP591.pdf30 Nov 2011: The Annals of Applied Probability2009, Vol. 19, No. 5, 1719–1780DOI: 10.1214/08-AAP591 Institute of Mathematical Statistics, 2009. STATE SPACE COLLAPSE AND DIFFUSION APPROXIMATIONFOR A NETWORK OPERATING UNDER A FAIR BANDWIDTH. SHARING POLICY. BY -
The Move-to-Front Rule for Multiple Lists
https://www.statslab.cam.ac.uk/~rrw1/publications/Courcoubetis%20-%20Weber%201990%20The%20move-to-front%20rule%20for%20multiple%20lists.pdf15 Sep 2011: However,in the following section we discuss variations of the problem in which the op-timal policy does indeed lie within the class of partition policies. -
Mathematical Foundations of Infinite-Dimensional Statistical Models
https://www.statslab.cam.ac.uk/~nickl/Site/__files/FULLPDF.pdf25 Feb 2020: Mathematical Foundations of Infinite-DimensionalStatistical Models. In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood and Bayesianposterior inference does -
1034 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. ...
https://www.statslab.cam.ac.uk/~rrw1/publications/Courcoubetis%20-%20Weber%202006%20%20Incentives%20for%20large%20peer-to-peer%20systems.pdf15 Sep 2011: Appendix I contains a derivation of the op-timization problem of maximizing expected social welfare,and Appendix II justifies the fact that it can be solve usingLagrangian methods. ... APPENDIX IIJUSTIFICATION FOR USE OF LAGRANGIAN METHODS. We prove that -
arXiv:math.PR/0001057 v1 11 Jan 2000
https://www.statslab.cam.ac.uk/~grg/teaching/doyle.pdf14 Dec 2005: arX. iv:m. ath. PR/0. 0010. 57 v. 1 1. 1 Ja. n 20. 00! " # $ % & ' ( ) , -. & /. " 0 ) ) ). 1 % 2 0 " $ 3 4 5 6 1 7 8 9 9 9 ) ) ) :! 5! " ; ( < % 0 =! & > - (? / " $! @ &! = =<! " $ A! > B " % C D % " E 6 # 7. 1 % 2 0 " $ 3 4 5 6 1 7 8 9 F G H 4! I. -
climb.dvi
https://www.statslab.cam.ac.uk/~grg/teaching/peres99probability.pdf14 Dec 2005: "!$#&%'() ,. -. /10 2&354 6798"79:. ;=<?>A@CBEDF<AGIHJ@K@CLJ<NMOHEPQR@TSVUXWOBEDYMZBE[[<]DMV>_LJWWVUbaOcEBdUfegRhdhdijkW_@l<AG5m_DF<_mH]DF<?npo"Pf@qLsrKPt[NPX@CDuPvGIwkHx@ly?WOBEDzHJG)H{QJn|rHx}EPn;<A}dPtQ. ]]{_{Rdd! "$#%&' )(", "-&.0/21
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