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Data-driven Particle Filters for Particle Markov Chain Monte Carlo

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thesis
posted on 19.06.2017 by KIN PONG LEUNG
This thesis proposes two computationally efficient algorithms suitable for analysing complex time series models under the Bayesian statistical paradigm. Certain critical theoretical properties of the algorithms are established, with their superior empirical performance demonstrated via a suite of controlled simulation experiments. A comprehensive review of the literature is provided, and the performance of many existing methods compared with that of the proposed algorithms. The thesis explores three particular classes of time series models, however the approach is general and finds application across the economic and other social science disciplines, as well as disciplines from the physical sciences.

History

Campus location

Australia

Principal supervisor

Catherine Scipione Forbes

Additional supervisor 1

Gael Margaret Martin

Year of Award

2017

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

Doctorate

Faculty

Faculty of Business and Economics

Exports