Abstract
| Abstract: The problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo algorithm is critical to the quality of the generated samples. Much work has gone into obtaining such results for various Metropolis-Hastings (MH) algorithms. Recently, acceptance probabilities other than MH are being employed in problems with intractable target distributions. There is little resource available on tuning the Gaussian proposal distributions for this situation. We obtain optimal scaling results for a general class of acceptance functions, which includes Barker's and Lazy-MH acceptance functions. In particular, optimal values for Barker's algorithm are derived and are found to be significantly different from that obtained for MH algorithms. |
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The aim of this special issue is to feature research papers on theory, methodology, and applications of models and methods for recent advances in statistical finance. We encourage submissions presenting original works on statistical, computational, and mathematical approaches to modelling and analysis of financial data. Innovative applications and case studies in financial statistics are welcome, especially related to novel methodological challenges in the treatment of big data and high-frequency data.
This special issue will bring together contributions from practitioners and researchers working on different aspects of statistical methods in finance, with methodological interests encompassing, but not limited to, the following domains:
The motivating application areas could be: For More Detail ...If you are a student and want your paper to be considered for student paper competition, then ask your supervisor to send a mail at statfin@cmi.ac.in, with a particular mention that you were the primary contributor and author of the paper by May 15, 2021.
You must submit your paper by May 15, 2021, to be considered for the competition. Mail your paper at statfin@cmi.ac.in
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