Open Access
Sequential analysis: some classical problems and new challenges
TLDR
A brief review of the developments in several classical problems of sequential analysis and their applications to biomedicine, economics and engi- neering can be found in this paper.Abstract:
We give a brief review of the developments in several classical problems of sequential analysis and their applications to biomedicine, economics and engi- neering. Even though it can only focus on a limited number of topics, the review shows that sequential analysis is still a vibrant subject after six decades of contin- ual development, with fresh ideas brought in from various fields of application and through interactions with other branches of statistics and probability. We conclude with some remarks on the opportunities and challenges ahead.read more
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Network Security: A Decision and Game-Theoretic Approach
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Analysis of Recursive Stochastic Algorithms
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Fixed-width output analysis for Markov chain Monte Carlo
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References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Journal ArticleDOI
A Simple Sequentially Rejective Multiple Test Procedure
TL;DR: In this paper, a simple and widely accepted multiple test procedure of the sequentially rejective type is presented, i.e. hypotheses are rejected one at a time until no further rejections can be done.
Journal ArticleDOI
A Stochastic Approximation Method
Herbert Robbins,Sutton Monro +1 more
TL;DR: In this article, a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability is presented.
Journal Article
Evaluation of survival data and two new rank order statistics arising in its consideration.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.