Lectures on probability theory and statistics

By E. Bolthausen, A. van der Vaart, Edwin A. Perkins. 2002

221 citations recorded by the source corpus.

Subfields: Condensed Matter Physics, Management Science and Operations Research, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/2f7782eab426c9354493f33e34b4fd8fb1b6ac8a

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In Condensed Matter Physics

In Management Science and Operations Research

In Mathematical Physics

Citations in the corpus

FINITELY RAMIFIED GRAPH-DIRECTED FRACTALS, SPECTRAL ASYMPTOTICS AND THE MULTIDIMENSIONAL RENEWAL THEOREM (2003, background): “In the light of the Einstein relation (see [1]), we see that θσ is the time scaling factor for the approximating random walk on the cell Kσ.”
Gaussian heat kernel upper bounds via the Phragmén–Lindelöf theorem (2006, unknown): “Open question Can one treat in a similar way the so-called sub-Gaussian estimates (see, for instance, [7, 19]), which are typical of fractals, namely, can one imagine that, with the same notation as in condition (3.”
Theory of classification : a survey of some recent advances (2005, unknown): “Closely related concentration results have been obtained in various ways including information-theoretic methods (see Ahlswede, Gács, and Körner [1], Marton [154–156], Dembo [69], Massart [158] and Rio [183]), Talagrand’s induction method [211,214,215] (see also McDiarmid [168], Luczak and McDiarmid”
Probability on Trees and Networks (2017, methodology): “◀ According to Schonmann (1999a), techniques of that paper combined with those of Stacey (1996) can be used to show the same inequality as that of Theorem 7.”
Higher Order Tangent Spaces and Influence Functions (2014, background): “pt of inﬂuence function of an estimator was originally coined in the theory of robust statistics, and as an asymptotic inﬂuence function played a role in the development of semiparametric statistics ([2, 3]). If an estimator Tn of a quantity µbased on a random sample of observations X1,X2,...,Xn pos”
Statistical performance of support vector machines (2008, background): “Under a Gaussian noise assumption, the behavior of the regularized least squares estimator of the form (3.4) (with the square loss ‘ ( x, y ) = ( x − y ) 2 replacing the hinge loss) is completely elucidated (see [ 25], Section 4.4).”