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
8 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.
Introduces Hellinger distance and negative exponential disparity robust estimators for polychoric correlation under complex surveys, with penalized variants, consistency theory, and simulation comparisons under contamination.
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.
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
Proves reverse Poincaré inequality on global attractor of 2D reaction-diffusion system to obtain near-parametric statistical recovery of initial conditions from discrete observations.
citing papers explorer
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Robust Estimation of Polychoric Correlation for Complex Survey Designs Using Minimum Divergence Methods
Introduces Hellinger distance and negative exponential disparity robust estimators for polychoric correlation under complex surveys, with penalized variants, consistency theory, and simulation comparisons under contamination.
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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.
-
Constrained Weighted Bayesian Bootstrap
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.