On the Bayesian Estimation of Parameters of SQDM

Murtada Ali Maqdisi

doi:https://doi.org/10.54216/NIF.030105

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Volume 3 • Issue 1 • PP: 24–28 • 2024

On the Bayesian Estimation of Parameters of SQDM

Murtada Ali Maqdisi ^1*

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¹College of Pharmacy, AL-Farahidi University, Baghdad, Iraq

* Corresponding Author.

DOI https://doi.org/10.54216/NIF.030105

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Received: May 05, 2023 Accepted: January 17, 2024

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Abstract

This work is concerned with the problem of estimating parameters of spatial quadratic models by the Bayesian technique (SQDM). This technique involves prior information on the first and second moments of the parameters, where its estimation model is called the Bayesian quadratic unbiased estimator. The results of the estimation are compared with the estimates of minimum-norm quadratic unbiased estimators.

Keywords

Bayesian estimation Parameter Estimation Estimator

References

[1] Adnan Shamkhi Jaber and Dhawiya Salman Hassan. In-troduction to Operations Research. Ministry of Higher Education and Scientific Research, University of Bagh-dad, Baghdad, Iraq, 1988.

[2] Nibal Sabah Abdul Rahman. Linear spatial heterogene-ity model with application. Master’s thesis, University of Mosul, Mosul, Iraq, 2001.

[3] Yunus. Bayes estimation of the spatial covariance func-tion with two parameters and three parameters. Master’s thesis, University of Mosul, Mosul, Iraq, 1996.

[4] Noel A. C. Cressie. Statistics for Spatial Data. John Wiley, New York, 1993.

[5] P. Delfiner. Linear estimation of nonstationary spatial phenomena. In M. Guarascio, M. David, and C. Hui-jbregts, editors, Advanced Geostatistics in the Mining Industry, pages 49–68. D. Reidel Publishing Co., Hol-land, 1976.

[6] W. S. Davies. Quantitative methods: Bayesian inference. Progress in Human Geography, 26(4):553, 2002.

[7] P. J. Diggle. Bayesian inference in gaussian model geo-statistics. Geographical and Environmental Modelling, 6(2):129, 2002.

[8] Robert V. Hogg and Allen T. Craig. Introduction to Mathematical Statistics. Macmillan Publishing Co., New York, 1978.

[9] J. Kleffe and R. Pincus. Bayes and best quadratic unbi-ased estimators for parameters of the covariance matrix in a normal linear model. Mathematische Operations-forschung und Statistik, 5(1):43–67, 1974.

[10] D. G. Krige. Some basic consideration in the application of geostatistics to the valuation of ore in south african gold mines. Journal of the South African Institute of Mining and Metallurgy, pages 383–391, 1976.

[11] R. J. Marshall and K. V. Mardia. Minimum norm quadratic estimation of components of spatial covari-ance. Mathematical Geology, 17(5):517–525, 1985.

[12] C. R. Rao and J. Kleffe. Estimation of Variance Com-ponents and Applications. North-Holland, New York, 1988.

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Maqdisi, Murtada Ali. "On the Bayesian Estimation of Parameters of SQDM." Neutrosophic and Information Fusion, vol. 3, no. 1, 2024, pp. 24–28. DOI: https://doi.org/10.54216/NIF.030105

Maqdisi, M. (2024). On the Bayesian Estimation of Parameters of SQDM. Neutrosophic and Information Fusion, Volume 3(Issue 1), 24–28. DOI: https://doi.org/10.54216/NIF.030105

Maqdisi, Murtada Ali. "On the Bayesian Estimation of Parameters of SQDM." Neutrosophic and Information Fusion Volume 3, no. Issue 1 (2024): 24–28. DOI: https://doi.org/10.54216/NIF.030105

Maqdisi, M. (2024) 'On the Bayesian Estimation of Parameters of SQDM', Neutrosophic and Information Fusion, Volume 3(Issue 1), pp. 24–28. DOI: https://doi.org/10.54216/NIF.030105

Maqdisi M. On the Bayesian Estimation of Parameters of SQDM. Neutrosophic and Information Fusion. 2024;Volume 3(Issue 1):24–28. DOI: https://doi.org/10.54216/NIF.030105

M. Maqdisi, "On the Bayesian Estimation of Parameters of SQDM," Neutrosophic and Information Fusion, vol. Volume 3, no. Issue 1, pp. 24–28, 2024. DOI: https://doi.org/10.54216/NIF.030105

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