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The subset argument and consistency of MLE in GLMM: Answer to an open problem and beyond
Cramer consistency crossed random effects MLE GLMM,salamander mating data subset argument Wald consistency
2013/4/27
We give answer to an open problem regarding consistency of the maximum likelihood estimators (MLEs) in generalized linear mixed models (GLMMs) involving crossed random effects. The solution to the ope...
On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit
On a link between kernel mean maps Fraunhofer diffraction an application super-resolution beyond the diffraction limit
2013/4/28
We establish a link between Fourier optics and a recent construction from the machine learning community termed the kernel mean map. Using the Fraunhofer approximation, it identifies the kernel with t...
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
Stochastic Bandits Beyond KL-UCB
2011/3/21
This paper presents a finite-time analysis of the KL-UCB algorithm, an online, horizon-free index policy for stochastic bandit problems. We prove two distinct results: first, for arbitrary bounded rew...
Percolation Beyond Z^d, Many Questions And a Few Answers
Percolation criticality planar graph transitive graph isoperimeteric inequality
2009/5/12
A comprehensive study of percolation in a more general context than the usual $Z^d$ setting is proposed, with particular focus on Cayley graphs, almost transitive graphs, and planar graphs. Results co...
Orthogonality and probability: beyond nearest neighbor transitions
reversible Markov chains orthogonal polynomials Karlin-McGregor representation
2009/4/29
In this article, we will explore why Karlin-McGregor method of using orthogonal polynomials in the study of Markov processes was so successful for one dimensional nearest neighbor processes, but faile...
Orthogonality and probability: beyond nearest neighbor transitions
beyond nearest Orthogonality
2009/4/22
In this article, we will explore why Karlin-McGregor method of using orthogonal polynomials in the study of Markov processes was so successful for one dimensional nearest neighbor processes, but faile...
