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Martingale Limit Theory and Its Application
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The article was published on 2014-09-23 and is currently open access. It has received 3075 citations till now. The article focuses on the topics: Local martingale & Doob's martingale inequality.read more
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Stochastic Consensus Seeking With Noisy and Directed Inter-Agent Communication: Fixed and Randomly Varying Topologies
Minyi Huang,Jonathan H. Manton +1 more
TL;DR: This work considers consensus seeking of networked agents on directed graphs where each agent has only noisy measurements of its neighbors' states and uses Stochastic approximation type algorithms to generalize the algorithm to networks with random link failures and prove convergence results.
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Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model
TL;DR: In this paper, the authors consider the conditional Gaussian likelihood as a stochastic process in the parameters and prove that it converges in distribution when errors are i.i.d. with suitable moment conditions and initial values are bounded.
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Limit theorems for weighted samples with applications to sequential Monte Carlo methods
Randal Douc,Eric Moulines +1 more
TL;DR: In this article, the authors introduce the concepts of weighted sample consistency and asymptotic normality, and derive conditions under which the transformations of the weighted sample used in the SMC algorithm preserve these properties.
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Adaptive importance sampling in monte carlo integration
Man Suk Oh,James O. Berger +1 more
TL;DR: In this paper, an adaptive importance sampling (AIS) scheme is introduced to compute integrals of the form as a mechanical, yet flexible, way of dealing with the selection of parameters of the importance function.
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Monitoring changes in linear models
TL;DR: In this paper, two classes of monitoring schemes are proposed to detect structural change in a linear model after a training period of size m. The first class of procedures is based on weighted CUSUMs of residuals, in which the unknown in-control parameter has been replaced by its least-squares estimate from the training sample, whereas the second class of schemes makes use of recursive residuals.