Moments and random number generation for the truncated elliptical family of distributions
This paper proposes an algorithm to generate random numbers from any member of the truncated multivariate elliptical family of distributions with a strictly decreasing density generating function. Based on Neal (2003) and Ho et al. (2012), we construct an efficient sampling method by means of a slic...
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| creator | Valeriano, Katherine A. L Galarza, Christian E Matos, Larissa A |
| description | This paper proposes an algorithm to generate random numbers from any member
of the truncated multivariate elliptical family of distributions with a
strictly decreasing density generating function. Based on Neal (2003) and Ho et
al. (2012), we construct an efficient sampling method by means of a slice
sampling algorithm with Gibbs sampler steps. We also provide a faster approach
to approximate the first and the second moment for the truncated multivariate
elliptical distributions where Monte Carlo integration is used for the
truncated partition, and explicit expressions for the non-truncated part
(Galarza et al., 2020). Examples and an application to environmental spatial
data illustrate its usefulness. Methods are available for free in the new R
library elliptical. |
| doi_str_mv | 10.48550/arxiv.2112.09319 |
| format | Article |
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of the truncated multivariate elliptical family of distributions with a
strictly decreasing density generating function. Based on Neal (2003) and Ho et
al. (2012), we construct an efficient sampling method by means of a slice
sampling algorithm with Gibbs sampler steps. We also provide a faster approach
to approximate the first and the second moment for the truncated multivariate
elliptical distributions where Monte Carlo integration is used for the
truncated partition, and explicit expressions for the non-truncated part
(Galarza et al., 2020). Examples and an application to environmental spatial
data illustrate its usefulness. Methods are available for free in the new R
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of the truncated multivariate elliptical family of distributions with a
strictly decreasing density generating function. Based on Neal (2003) and Ho et
al. (2012), we construct an efficient sampling method by means of a slice
sampling algorithm with Gibbs sampler steps. We also provide a faster approach
to approximate the first and the second moment for the truncated multivariate
elliptical distributions where Monte Carlo integration is used for the
truncated partition, and explicit expressions for the non-truncated part
(Galarza et al., 2020). Examples and an application to environmental spatial
data illustrate its usefulness. Methods are available for free in the new R
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of the truncated multivariate elliptical family of distributions with a
strictly decreasing density generating function. Based on Neal (2003) and Ho et
al. (2012), we construct an efficient sampling method by means of a slice
sampling algorithm with Gibbs sampler steps. We also provide a faster approach
to approximate the first and the second moment for the truncated multivariate
elliptical distributions where Monte Carlo integration is used for the
truncated partition, and explicit expressions for the non-truncated part
(Galarza et al., 2020). Examples and an application to environmental spatial
data illustrate its usefulness. Methods are available for free in the new R
library elliptical.</abstract><doi>10.48550/arxiv.2112.09319</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Computation Statistics - Methodology |
| title | Moments and random number generation for the truncated elliptical family of distributions |
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