Search results for "Strong consistency"

showing 2 items of 2 documents

Parameter Estimation for α-Fractional Bridges

2013

Let α, T > 0. We study the asymptotic properties of a least squares estimator for the parameter α of a fractional bridge defined as \(\mathrm{d}X_{t} = -\alpha \, \frac{X_{t}} {T-t}\,\mathrm{d}t + \mathrm{d}B_{t}\), 0 ≤ t \frac{1} {2}\). Depending on the value of α, we prove that we may have strong consistency or not as t → T. When we have consistency, we obtain the rate of this convergence as well. Also, we compare our results to the (known) case where B is replaced by a standard Brownian motion W.

CombinatoricsPhysicssymbols.namesakeFractional Brownian motionWiener processEstimation theoryConsistency (statistics)symbolsStrong consistencyBrownian motion
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Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

2020

We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS approach provides a natural alternative to delayed acceptance (DA) pseudo-marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelisation and additional flexibility in MCMC implementation. We detail minimal conditions which ensure strong consistency of the sug…

Statistics and ProbabilityHyperparameter05 social sciencesBayesian probabilityStrong consistencyEstimatorContext (language use)Markov chain Monte Carlo01 natural sciencesStatistics::Computation010104 statistics & probabilitysymbols.namesake0502 economics and businesssymbols0101 mathematicsStatistics Probability and UncertaintyParticle filterAlgorithmImportance sampling050205 econometrics MathematicsScandinavian Journal of Statistics
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