The LRT statistic converges in distribution to the supremum of a bar-chi-squared process under the null and a noncentral version under local alternatives, with the same form whether or not the information matrix is singular due to the nuisance parameter.
Convergence Rate of Sieve Estimates.The Annals of Statistics, 22(2):580–615, June 1994
5 Pith papers cite this work. Polarity classification is still indexing.
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Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
The work introduces subsampling confidence bounds for persistence diagrams of time-delay embeddings and an asymptotically valid test for periodicity that performs comparably to Lomb-Scargle on periodic data and better on chirps.
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Asymptotics for likelihood ratio tests of boundary points with singular information and unidentifiable nuisance parameters
The LRT statistic converges in distribution to the supremum of a bar-chi-squared process under the null and a noncentral version under local alternatives, with the same form whether or not the information matrix is singular due to the nuisance parameter.
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Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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Subsampling Confidence Bound for Persistent Diagram via Time-delay Embedding
The work introduces subsampling confidence bounds for persistence diagrams of time-delay embeddings and an asymptotically valid test for periodicity that performs comparably to Lomb-Scargle on periodic data and better on chirps.
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