Moment matching adaptive importance sampling with skew-student proposals

Wang Shijia () and Swartz Tim ()
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Wang Shijia: School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, No 94 Weijin Road, Tianjin 300071, P. R. China
Swartz Tim: Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada

Monte Carlo Methods and Applications, 2022, vol. 28, issue 2, 149-162

Abstract: This paper considers integral approximation via importance sampling where the importance sampler is chosen from a family of skew-Student distributions. This is an alternative class of distributions than is typically considered in importance sampling applications. We describe variate generation and propose adaptive methods for fitting a member of the skew-Student family to a particular integral. We also demonstrate the utility of the approach in several examples.

Keywords: Adaptive algorithms; importance sampling; simulation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:28:y:2022:i:2:p:149-162:n:2

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DOI: 10.1515/mcma-2022-2106

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