Bibliografías temáticas / Spatial analysis (Statistics) Regression analysis. Bayesian statistical decision theory

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Literatura académica sobre el tema "Spatial analysis (Statistics) Regression analysis. Bayesian statistical decision theory"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 18 de febrero de 2022

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Artículos de revistas sobre el tema "Spatial analysis (Statistics) Regression analysis. Bayesian statistical decision theory"

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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN". PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, n.º 2 (1 de junio de 2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of global (Bayesian). Survival Bayesial parameter estimation using MCMC algorithms for model complex / complicated and difficult to resolve while the Cox regression models using the method of partial likelihood. Results of the parameter estimates do not close form that needs to be done by the method of Newton-Raphson iteration.
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Tesis sobre el tema "Spatial analysis (Statistics) Regression analysis. Bayesian statistical decision theory"

1

Wheeler, David C. "Diagnostic tools and remedial methods for collinearity in linear regression models with spatially varying coefficients". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155413322.

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Oleson, Jacob J. "Bayesian spatial models for small area estimation /". free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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McBride, John Jacob Bratcher Thomas L. "Conjugate hierarchical models for spatial data an application on an optimal selection procedure /". Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/3955.

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Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology". Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.

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Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
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Woodard, Roger. "Bayesian hierarchical models for hunting success rates /". free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.

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Sun, Xiaoqian. "Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project". Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4477.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 1, 2007) Vita. Includes bibliographical references.
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7

Higdon, David. "Spatial applications of Markov chain Monte Carlo for Bayesian inference /". Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8942.

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Miyamoto, Kazutoshi Seaman John Weldon. "Bayesian and maximum likelihood methods for some two-segment generalized linear models". Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5233.

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Powers, Stephanie L. Stamey James D. "Bayesian approach to inference and variable selection for misclassified and under-reported response models". Waco, Tex. : Baylor University, 2009. http://hdl.handle.net/2104/5355.

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Byers, Simon. "Bayesian modeling of highly structured systems using Markov chain Monte Carlo /". Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8980.

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Libros sobre el tema "Spatial analysis (Statistics) Regression analysis. Bayesian statistical decision theory"

1

Thomas, Kneib, ed. Bayesian smoothing and regression for longitudinal, spatial and event history data. Oxford: Oxford University Press, 2011.

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2

Guttman, Irwin. Bayesian assessment of assumptions of regression analysis. Toronto: University of Toronto, Dept. of Statistics, 1988.

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Bagchi, Parthasarathy. Bayesian assessment of assumptions of regression analysis. Toronto: University of Toronto, Dept. of Statistics, 1989.

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4

Wakefield, Jon. Bayesian and Frequentist Regression Methods. New York, NY: Springer New York, 2013.

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5

Gomez-Rubio, Virgilio. Bayesian Inference with INLA. Boca Raton, Florida, USA: Taylor & Francis Group, 2020.

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Brunner, Lawrence J. Bayesian linear regression with error terms that have symmetric unimodal densities. Toronto: Department of Statistics, University of Toronto, 1989.

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Fei xian xing zi hui gui mo xing de fei can shu fang fa ji ying yong. Beijing: Ke xue chu ban she, 2013.

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Guttman, Irwin. Estimating the number of aberrant laboratories. Toronto, Ont: University of Toronto, Dept. of Statistics, 1993.

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Blangiardo, Marta. Spatial and spatio-temporal Bayesian models with R-INLA. Chichester, West Sussex: John Wiley and Sons, Inc., 2015.

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Saleh, A. K. Md. Ehsanes. Theory of Preliminary Test and Stein-Type Estimation with Applications. New York: John Wiley & Sons, Ltd., 2006.

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