Results 51 to 60 of about 7,698 (239)
A unified approach to Stein’s method for stable distributions [PDF]
Probability Surveys, 2020 In this article, we propose a modified technique for finding Stein operator for the class of infinitely divisible distributions using its characteristic function that relaxes the assumption of the first finite moment.
Neelesh Shankar Upadhye, K. Barman
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arXiv: Probability, 2014 The paper presents a general introduction to the astonishing method for deriving probability approximations that was invented by Charles Stein around 50 years ago.
Barbour, A D, Chen, L H Y
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Electronic Journal of Probability, 2020 Applying an inductive technique for Stein and zero bias couplings yields Berry-Esseen theorems for normal approximation for two new examples. The conditions of the main results do not require that the couplings be bounded. Our two applications, one to the Erd s-R nyi, random graph with a fixed number of edges, and one to Jack measure on tableaux ...
Chen, L.H.Y., Goldstein, L., Röllin, A.
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Stein’s method, logarithmic Sobolev and transport inequalities [PDF]
Geometric and Functional Analysis, 2014 We develop connections between Stein’s approximation method, logarithmic Sobolev and transport inequalities by introducing a new class of functional inequalities involving the relative entropy, the Stein kernel, the relative Fisher information and the ...
M. Ledoux, I. Nourdin, G. Peccati
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Convergence rates for a class of estimators based on Stein’s method [PDF]
Bernoulli, 2016 Gradient information on the sampling distribution can be used to reduce the variance of Monte Carlo estimators via Stein's method. An important application is that of estimating an expectation of a test function along the sample path of a Markov chain ...
C. Oates +3 more
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Stein interpolation for the real interpolation method [PDF]
Banach Journal of Mathematical Analysis, 2021 AbstractWe prove a complex formulation of the real interpolation method, showing that the real and complex interpolation methods are not inherently real or complex. Using this complex formulation, we prove Stein interpolation for the real interpolation method.
Nick Lindemulder, Emiel Lorist
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Stein’s method for the law of large numbers under sublinear expectations [PDF]
Probability, Uncertainty and Quantitative Risk, 2019 Peng, S. [6] proved the law of large numbers under a sublinear expectation. In this paper, we give its error estimates by Stein’s method.
Yongsheng Song
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Stein's Method Meets Malliavin Calculus: A Short Survey With New Estimates [PDF]
, 2010 We provide an overview of some recent techniques involving the Malliavin calculus of variations and the so-called ``Stein's method'' for the Gaussian approximations of probability distributions.
Ivan Nourdin, Giovanni Peccati
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Stein meets Malliavin in normal approximation [PDF]
, 2015 Stein's method is a method of probability approximation which hinges on the solution of a functional equation. For normal approximation the functional equation is a first order differential equation.
Chen, Louis H. Y.
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A Nonuniform Bound to an Independent Test in High Dimensional Data Analysis via Stein’s Method
Journal of Probability and Statistics, 2019 The Berry-Esseen bound for the random variable based on the sum of squared sample correlation coefficients and used to test the complete independence in high diemensions is shown by Stein’s method.
Nahathai Rerkruthairat
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