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Likelihood-based inference in s-distributions

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Likelihood-based inference in s-distributions. / Tsionas, Efthymios.
In: Communications in Statistics - Theory and Methods, Vol. 44, No. 1, 2015, p. 153-158.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, E 2015, 'Likelihood-based inference in s-distributions', Communications in Statistics - Theory and Methods, vol. 44, no. 1, pp. 153-158. https://doi.org/10.1080/03610926.2012.731129

APA

Tsionas, E. (2015). Likelihood-based inference in s-distributions. Communications in Statistics - Theory and Methods, 44(1), 153-158. https://doi.org/10.1080/03610926.2012.731129

Vancouver

Tsionas E. Likelihood-based inference in s-distributions. Communications in Statistics - Theory and Methods. 2015;44(1):153-158. Epub 2014 Dec 1. doi: 10.1080/03610926.2012.731129

Author

Tsionas, Efthymios. / Likelihood-based inference in s-distributions. In: Communications in Statistics - Theory and Methods. 2015 ; Vol. 44, No. 1. pp. 153-158.

Bibtex

@article{a4d6d18dc8094ebfa1f407f9a6aad102,
title = "Likelihood-based inference in s-distributions",
abstract = "In this paper, we propose new estimation techniques in connection with the system of S-distributions. Besides “exact” maximum likelihood (ML), we propose simulated ML and a characteristic function-based procedure. The “exact” and simulated likelihoods can be used to provide numerical, MCMC-based Bayesian inferences.",
keywords = "Bayesian inference, Likelihood function, MCMC, Posterior distribution, S-distributions",
author = "Efthymios Tsionas",
year = "2015",
doi = "10.1080/03610926.2012.731129",
language = "English",
volume = "44",
pages = "153--158",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Likelihood-based inference in s-distributions

AU - Tsionas, Efthymios

PY - 2015

Y1 - 2015

N2 - In this paper, we propose new estimation techniques in connection with the system of S-distributions. Besides “exact” maximum likelihood (ML), we propose simulated ML and a characteristic function-based procedure. The “exact” and simulated likelihoods can be used to provide numerical, MCMC-based Bayesian inferences.

AB - In this paper, we propose new estimation techniques in connection with the system of S-distributions. Besides “exact” maximum likelihood (ML), we propose simulated ML and a characteristic function-based procedure. The “exact” and simulated likelihoods can be used to provide numerical, MCMC-based Bayesian inferences.

KW - Bayesian inference

KW - Likelihood function

KW - MCMC

KW - Posterior distribution

KW - S-distributions

U2 - 10.1080/03610926.2012.731129

DO - 10.1080/03610926.2012.731129

M3 - Journal article

VL - 44

SP - 153

EP - 158

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 1

ER -