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The normal law under linear restrictions: simulation and estimation via minimax tilting

Botev, Z. I.

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2017-01, Vol.79 (1), p.125-148 [Periódico revisado por pares]

Oxford: John Wiley & Sons Ltd

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Título:
The normal law under linear restrictions: simulation and estimation via minimax tilting
Autor: Botev, Z. I.
Assuntos: Computer simulation ; Data simulation ; Estimators ; Exact simulation ; Exponential tilting ; Linear inequalities ; Markov analysis ; Mathematical models ; Methodology ; Minimax technique ; Monte Carlo methods ; Monte Carlo simulation ; Multivariate analysis ; Multivariate normal distribution ; Normal distribution ; Polytope probabilities ; Probit posterior simulation ; Simulation ; Studies
É parte de: Journal of the Royal Statistical Society. Series B, Statistical methodology, 2017-01, Vol.79 (1), p.125-148
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain Monte Carlo sampling. We propose a minimax tilting method for exact independently and identically distributed data simulation from the truncated multivariate normal distribution. The new methodology provides both a method for simulation and an efficient estimator to hitherto intractable Gaussian integrals. We prove that the estimator has a rare vanishing relative error asymptotic property. Numerical experiments suggest that the scheme proposed is accurate in a wide range of set-ups for which competing estimation schemes fail. We give an application to exact independently and identically distributed data simulation from the Bayesian posterior of the probit regression model.
Editor: Oxford: John Wiley & Sons Ltd
Idioma: Inglês

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The normal law under linear restrictions: simulation and estimation via minimax tilting

Botev, Z. I.

Oxford: John Wiley & Sons Ltd

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