Dissertations / Theses: 'Bayesian statistical analysi'

1

Ma, Yimin. "Bayesian and empirical Bayesian analysis for the truncation parameter distribution families /." *McMaster only, 1998.

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CHIESA, DAVIDE. "Development and experimental validation of a Monte Carlo simulation model for the Triga Mark II reactor." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/50064.

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In recent years, many computer codes, based on Monte Carlo methods or deterministic calculations, have been developed to separately analyze different aspects regarding nuclear reactors. Nuclear reactors are very complex systems, which require an integrated analysis of all the variables which are intrinsically correlated: neutron fluxes, reaction rates, neutron moderation and absorption, thermal and power distributions, heat generation and transfer, criticality coefficients, fuel burnup, etc. For this reason, one of the main challenges in the analysis of nuclear reactors is the coupling of neutronics and thermal-hydraulics simulation codes, with the purpose of achieving a good modeling and comprehension of the mechanisms which rule the transient phases and the dynamic behavior of the reactor. This is very important to guarantee the control of the chain reaction, for a safe operation of the reactor. In developing simulation tools, benchmark analyses are needed to prove the reliability of the simulations. The experimental measurements conceived to be compared with the results coming out from the simulations are really precious and can provide useful information to improve the description of the physics phenomena in the simulation models. My PhD research activity was held in this framework, as part of the research project Analysis of Reactor COre (ARCO, promoted by INFN) whose task was the development of modern, flexible and integrated tools for the analysis of nuclear reactors, relying on the experimental data collected at the research reactor TRIGA Mark II, installed at the Applied Nuclear Energy Laboratory (LENA) at the University of Pavia. In this way, once the effectiveness and the reliability of these tools for modeling an experimental reactor have been demonstrated, these could be applied to develop new generation systems. In this thesis, I present the complete neutronic characterization of the TRIGA Mark II reactor, which was analyzed in different operating conditions through experimental measurements and the development of a Monte Carlo simulation tool (relied on the MCNP code) able to take into account the ever increasing complexity of the conditions to be simulated. First of all, after giving an overview of some theoretical concepts which are fundamental for the nuclear reactor analysis, a model that reconstructs the first working period of the TRIGA Mark II reactor, in which the “fresh” fuel was not heavily contaminated with fission reaction products, is described. In particular, all the geometries and the materials are described in the MCNP simulation model with good detail, in order to reconstruct the reactor criticality and all the effects on the neutron distributions. The very good results obtained from the simulations of the reactor at low power condition -in which the fuel elements can be considered to be in thermal equilibrium with the water around them- are then used to implement a model for simulating the full power condition (250kW), in which the effects arising from the temperature increase in the fuel-moderator must be taken into account. The MCNP simulation model was exploited to evaluate the reactor power distribution and a dedicated experimental campaign was performed to measure the water temperature within the reactor core. In this way, through a thermal-hydraulic calculation tool, it has been possible to determine the temperature distribution within the fuel elements and to include the description of the thermal effects in the MCNP simulation model. Thereafter, since the neutron flux is a crucial parameter affecting the reaction rates and thus the fuel burnup, its energy and space distributions are analyzed presenting the results of several neutron activation measurements. Particularly, the neutron flux was firstly measured in the reactor's irradiation facilities through the neutron activation of many different isotopes. Hence, in order to analyze the energy flux spectra, I implemented an analysis tool, based on Bayesian statistics, which allows to combine the experimental data from the different activated isotopes and reconstruct a multi-group flux spectrum. Subsequently, the spatial neutron flux distribution within the core was measured by activating several aluminum-cobalt samples in different core positions, thus allowing the determination of the integral and fast flux distributions from the analysis of cobalt and aluminum, respectively. Finally, I present the results of the fuel burnup calculations, that were performed for simulating the current core configuration after a 48 years-long operation. The good accuracy that was reached in the simulation of the neutron fluxes, as confirmed by the experimental measurements, has allowed to evaluate the burnup of each fuel element from the knowledge of the operating hours and the different positions occupied in the core over the years. In this way, it has been possible to exploit the MCNP simulation model to determine a new optimized core configuration which could ensure, at the same time, a higher reactivity and the use of less fuel elements. This configuration was realized in September 2013 and the experimental results confirm the high quality of the work done. The results of this Ph.D. thesis highlight that it is possible to implement analysis tools -ranging from Monte Carlo simulations to the fuel burnup time evolution software, from neutron activation measurements to the Bayesian statistical analysis of flux spectra, and from temperature measurements to thermal-hydraulic models-, which can be appropriately exploited to describe and comprehend the complex mechanisms ruling the operation of a nuclear reactor. Particularly, it was demonstrated the effectiveness and the reliability of these tools in the case of an experimental reactor, where it was possible to collect many precious data to perform benchmark analyses. Therefore, for as these tools have been developed and implemented, they can be used to analyze other reactors and, possibly, to project and develop new generation systems, which will allow to decrease the production of high-level nuclear waste and to exploit the nuclear fuel with improved efficiency.

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Fung, Wing-kam Tony. "Analysis of outliers using graphical and quasi-Bayesian methods /." [Hong Kong] : University of Hong Kong, 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1236146X.

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4

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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5

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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馮榮錦 and Wing-kam Tony Fung. "Analysis of outliers using graphical and quasi-Bayesian methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31230842.

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7

Brody-Moore, Peter. "Bayesian Hierarchical Meta-Analysis of Asymptomatic Ebola Seroprevalence." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2228.

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The continued study of asymptomatic Ebolavirus infection is necessary to develop a more complete understanding of Ebola transmission dynamics. This paper conducts a meta-analysis of eight studies that measure seroprevalence (the number of subjects that test positive for anti-Ebolavirus antibodies in their blood) in subjects with household exposure or known case-contact with Ebola, but that have shown no symptoms. In our two random effects Bayesian hierarchical models, we find estimated seroprevalences of 8.76% and 9.72%, significantly higher than the 3.3% found by a previous meta-analysis of these eight studies. We also produce a variation of this meta-analysis where we exclude two of the eight studies. In this model, we find an estimated seroprevalence of 4.4%, much lower than our first two Bayesian hierarchical models. We believe a random effects model more accurately reflects the heterogeneity between studies and thus asymptomatic Ebola is more seroprevalent than previously believed among subjects with household exposure or known case-contact. However, a strong conclusion cannot be reached on the seriousness of asymptomatic Ebola without an international testing standard and more data collection using this adopted standard.

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Rivers, Derick Lorenzo. "Dynamic Bayesian Approaches to the Statistical Calibration Problem." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3599.

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The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM framework is used to capture the time-varying parameters that may be changing or drifting over time. Dynamic based approaches to the linear, nonlinear, and multivariate calibration problem are presented in this dissertation. Simulation studies are conducted where the dynamic models are compared to some well known "static'" calibration approaches in the literature from both the frequentist and Bayesian perspectives. Applications to microwave radiometry are given.

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陳潔妍 and Kit-yin Chan. "Bayesian analysis of wandering vector models for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31214939.

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Chan, Kit-yin. "Bayesian analysis of wandering vector models for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19977025.

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Delcroix, Sophie M. "Bayesian Analysis of Cancer Mortality Rates from Different Types and their Relative Occurrences." Digital WPI, 1999. https://digitalcommons.wpi.edu/etd-theses/1114.

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"We analyze mortality data from prostate, colon, lung, and all other types (called other cancer) to obtain age specific and age adjusted mortality rates for white males in the U.S. A related problem is to estimate the relative occurrences of these four types of cancer. We use Bayesian method because it permits a degree of smoothing which is needed to analyze data at a small area level and to assess the patterns. In the recent Atlas of the United States Mortality (1996) each type of cancer was analyzed individually. The difficulty in doing so is that there are many small areas with zero deaths. We conjecture that simultaneous analyses might help to overcome this problem, and at the same time to estimate the relative occurrences. We start with a Poisson model for the deaths, which produces a likelihood function that separates into two parts: a Poisson likelihood for the rates and a multinomial likelihood for the relative occurrences. These permit the use of a standard Poisson regression model on age as in Nandram, Sedransk and Pickle (1999), and the novelty is a multivariate logit model on the relative occurrences in which per capita income, the percent of people below poverty level, education (percent of people with four years of college) and two criteria pollutants, EPAPM25 and EPASO2, are used as covariates. We fitted the models using Markov chain Monte Carlo methods. We used one of the models to present maps of occurrences and rates for the four types. An alternative model did not work well because it provides the same pattern by age and disease. We found that while EPAPM25 has a negative effect on the occurrences, EPASO2 has a positive effect. Also, we found some interesting patterns associated with the geographical variations of mortality rates and the relative occurrences of the four cancer types."

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鄧沛權 and Pui-kuen Tang. "Bayesian analysis of errors-in-variables in generalized linear models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1992. http://hub.hku.hk/bib/B31232802.

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Tang, Pui-kuen. "Bayesian analysis of errors-in-variables in generalized linear models /." [Hong Kong : University of Hong Kong], 1992. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1325330X.

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14

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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15

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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Hills, Susan. "The parametrisation of statistical models." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329850.

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Righter, Emily Stewart. "Graphical and Bayesian Analysis of Unbalanced Patient Management Data." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1710.pdf.

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Eno, Daniel R. "Noninformative Prior Bayesian Analysis for Statistical Calibration Problems." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/27140.

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In simple linear regression, it is assumed that two variables are linearly related, with unknown intercept and slope parameters. In particular, a regressor variable is assumed to be precisely measurable, and a response is assumed to be a random variable whose mean depends on the regressor via a linear function. For the simple linear regression problem, interest typically centers on estimation of the unknown model parameters, and perhaps application of the resulting estimated linear relationship to make predictions about future response values corresponding to given regressor values. The linear statistical calibration problem (or, more precisely, the absolute linear calibration problem), bears a resemblance to simple linear regression. It is still assumed that the two variables are linearly related, with unknown intercept and slope parameters. However, in calibration, interest centers on estimating an unknown value of the regressor, corresponding to an observed value of the response variable. We consider Bayesian methods of analysis for the linear statistical calibration problem, based on noninformative priors. Posterior analyses are assessed and compared with classical inference procedures. It is shown that noninformative prior Bayesian analysis is a strong competitor, yielding posterior inferences that can, in many cases, be correctly interpreted in a frequentist context. We also consider extensions of the linear statistical calibration problem to polynomial models and multivariate regression models. For these models, noninformative priors are developed, and posterior inferences are derived. The results are illustrated with analyses of published data sets. In addition, a certain type of heteroscedasticity is considered, which relaxes the traditional assumptions made in the analysis of a statistical calibration problem. It is shown that the resulting analysis can yield more reliable results than an analysis of the homoscedastic model.
Ph. D.

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19

Lindsey, Heidi Lula. "An Introduction to Bayesian Methodology via WinBUGS and PROC MCMC." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2784.

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Bayesian statistical methods have long been computationally out of reach because the analysis often requires integration of high-dimensional functions. Recent advancements in computational tools to apply Markov Chain Monte Carlo (MCMC) methods are making Bayesian data analysis accessible for all statisticians. Two such computer tools are Win-BUGS and SASR 9.2's PROC MCMC. Bayesian methodology will be introduced through discussion of fourteen statistical examples with code and computer output to demonstrate the power of these computational tools in a wide variety of settings.

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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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Toman, Blaza. "Bayesian optimal experimental design for the comparison of treatment with a control in the analysis of variance setting /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487330761216343.

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22

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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23

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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24

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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Brink, Anton Meredith. "Bayesian analysis of contingency tables." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/8948.

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Murphy, James Kevin. "Hidden states, hidden structures : Bayesian learning in time series models." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/250355.

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This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4). Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6). Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7).

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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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Ounpraseuth, Songthip T. Young Dean M. "Selected topics in statistical discriminant analysis." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4883.

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29

Thaithara, Balan Sreekumar. "Bayesian methods for astrophysical data analysis." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607847.

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Yan, Jiajia. "Statistical analysis on diffusion tensor estimation." Thesis, University of Wolverhampton, 2017. http://hdl.handle.net/2436/621860.

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Diffusion tensor imaging (DTI) is a relatively new technology of magnetic resonance imaging, which enables us to observe the insight structure of the human body in vivo and non-invasively. It displays water molecule movement by a 3×3 diffusion tensor at each voxel. Tensor field processing, visualisation and tractography are all based on the diffusion tensors. The accuracy of estimating diffusion tensor is essential in DTI. This research focuses on exploring the potential improvements at the tensor estimation of DTI. We analyse the noise arising in the measurement of diffusion signals. We present robust methods, least median squares (LMS) and least trimmed squares (LTS) regressions, with forward search algorithm that reduce or eliminate outliers to the desired level. An investigation of the criterion to detect outliers is provided in theory and practice. We compare the results with the generalised non-robust models in simulation studies and applicants and also validated various regressions in terms of FA, MD and orientations. We show that the robust methods can handle the data with up to 50% corruption. The robust regressions have better estimations than generalised models in the presence of outliers. We also consider the multiple tensors problems. We review the recent techniques of multiple tensor problems. Then we provide a new model considering neighbours' information, the Bayesian single and double tensor models using neighbouring tensors as priors, which can identify the double tensors effectively. We design a framework to estimate the diffusion tensor field with detecting whether it is a single tensor model or multiple tensor model. An output of this framework is the Bayesian neighbour (BN) algorithm that improves the accuracy at the intersection of multiple fibres. We examine the dependence of the estimators on the FA and MD and angle between two principal diffusion orientations and the goodness of fit. The Bayesian models are applied to the real data with validation. We show that the double tensors model is more accurate on distinct fibre orientations, more anisotropic or similar mean diffusivity tensors. The final contribution of this research is in covariance tensor estimation. We define the median covariance matrix in terms of Euclidean and various non-Euclidean metrics taking its symmetric semi-positive definiteness into account. We compare with estimation methods, Euclidean, power Euclidean, square root Euclidean, log-Euclidean, Riemannian Euclidean and Procrustes median tensors. We provide an analysis of the different metric between different median covariance tensors. We also provide the weighting functions and define the weighted non-Euclidean covariance tensors. We finish with manifold-valued data applications that improve the illustration of DTI images in tensor field processing with defined non-weighted and weighted median tensors. The validation of non-Euclidean methods is studied in the tensor field processing. We show that the root square median estimator is preferable in general, which can effectively exclude outliers and clearly shows the important structures of the brain. The power Euclidean median estimator is recommended when producing FA map.

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Puza, Borek Dalibor. "Aspects of Bayesian biostatistics." Thesis, Canberra, ACT : The Australian National University, 1994. http://hdl.handle.net/1885/140911.

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Wakefield, Jon. "The Bayesian analysis of pharmacokinetic models." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334806.

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33

Welch, Jason. "Bayesian methods in chemical data analysis." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319893.

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34

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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35

Stark, J. Alex. "Statistical model selection techniques for data analysis." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390190.

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Husain, Syeda Tasmine. "Bayesian analysis of longitudinal models /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,163598.

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Sheng, Ru. "A Bayesian analysis of hypothesis testing problems with skewed alternatives." [Milwaukee, Wis.] : e-Publications@Marquette, 2009. http://epublications.marquette.edu/dissertations_mu/23.

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Maimon, Geva. "A Bayesian spatial analysis of glass data /." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82284.

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In criminal investigations involving glass evidence, refractive index (RI) is the property of glass most commonly used by forensic examiners to determine the association between control samples of glass obtained at the crime scene, and samples of glass found on a suspect. Previous studies have shown that an intrinsic variability of RI exists within a pane of float glass. In this thesis, we attempt to determine whether this variability is spatially determined or random in nature, the conclusion of which plays an important role in the statistical interpretation of glass evidence. We take a Bayesian approach in fitting a spatial model to our data, and utilize the WinBUGS software to perform Gibbs sampling. To test for spatial variability, we propose two test quantities, and employ Bayesian Monte Carlo significance tests to test our data, as well as nine other specifically formulated data-sets.

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Koh, You Beng, and 辜有明. "Bayesian analysis in Markov regime-switching models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521644.

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van Norden and Schaller (1996) develop a standard regime-switching model to study stock market crashes. In their seminal paper, they use the maximum likelihood estimation to estimate the model parameters and show that a two-regime speculative bubble model has significant explanatory power for stock market returns in some observed periods. However, it is well known that the maximum likelihood estimation can lead to bias if the model contains multiple local maximum points or the estimation starts with poor initial values. Therefore, a better approach to estimate the parameters in the regime-switching models is to be found. One possible way is the Bayesian Gibbs-sampling approach, where its advantages are well discussed in Albert and Chib (1993). In this thesis, the Bayesian Gibbs-sampling estimation is examined by using two U.S. stock datasets: CRSP monthly value-weighted index from Jan 1926 to Dec 2010 and S&P 500 index from Jan 1871 to Dec 2010. It is found that the Gibbs-sampling estimation explains the U.S. data better than the maximum likelihood estimation. Moreover, the existing standard regime-switching speculative behaviour model is extended by considering the time-varying transition probabilities which are governed by the first-order Markov chain. It is shown that the time-varying first-order transition probabilities of Markov regime-switching speculative rational bubbles can lead stock market returns to have a second-order Markov regime. In addition, a Bayesian Gibbs-sampling algorithm is developed to estimate the parameters in the second-order two-state Markov regime-switching model.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy

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40

Tra, Yolande Vololonirina. "Bayesian analysis for avian nest survival models /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974691.

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41

Adhikari, Kaustubh. "Statistical Methodology for Sequence Analysis." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10178.

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Rare disease variants are receiving increasing importance in the past few years as the potential cause for many complex diseases, after the common disease variants failed to explain a large part of the missing heritability. With the advancement in sequencing techniques as well as computational capabilities, statistical methodology for analyzing rare variants is now a hot topic, especially in case-control association studies. In this thesis, we initially present two related statistical methodologies designed for case-control studies to predict the number of common and rare variants in a particular genomic region underlying the complex disease. Genome-wide association studies are nowadays routinely performed to identify a few putative marker loci or a candidate region for further analysis. These methods are designed to work with SNP data on such a genomic region highlighted by GWAS studies for potential disease variants. The fundamental idea is to use Bayesian methodology to obtain bivariate posterior distributions on counts of common and rare variants. While the ﬁrst method uses randomly generated (minimal) ancestral recombination graphs, the second method uses ensemble clustering method to explore the space of genealogical trees that represent the inherent structure in the test subjects. In contrast to the aforesaid methods which work with SNP data, the third chapter deals with next-generation sequencing data to detect the presence of rare variants in a genomic region. We present a non-parametric statistical methodology for rare variant association testing, using the well-known Kolmogorov-Smirnov framework adapted for genetic data. it is a fast, model-free robust statistic, designed for situations where both deleterious and protective variants are present. It is also unique in utilizing the variant locations in the test statistic.

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Krnjajić, Milovan. "Contributions to Bayesian statistical analysis : model specification and nonparametric inference /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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43

Haro, Lopez Ruben Alejandro. "Data adaptive Bayesian analysis using distributional mixtures." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299509.

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44

Kim, Yong Ku. "Bayesian multiresolution dynamic models." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180465799.

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45

GUINDANI, MICHELE. "Bayesian non-parametric analysis of spatial data." Doctoral thesis, Università Bocconi, 2006. http://hdl.handle.net/11565/4050378.

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Ravindran, Palanikumar. "Bayesian Analysis of Circular Data Using Wrapped Distributions." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-10292002-150812/.

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Circular data arise in a number of different areas such as geological, meteorological, biological and industrial sciences. We cannot use standard statistical techniques to model circular data, due to the circular geometry of the sample space. One of the common methods used to analyze such data is the wrapping approach. Using the wrapping approach, we assume that, by wrapping a probability distribution from the real line onto the circle, we obtain the probability distribution for circular data. This approach creates a vast class of probability distributions that are flexible to account for different features of circular data. However, the likelihood-based inference for such distributions can be very complicated and computationally intensive. The EM algorithm used to compute the MLE is feasible, but is computationally unsatisfactory. Instead, we use Markov Chain Monte Carlo (MCMC) methods with a data augmentation step, to overcome such computational difficulties. Given a probability distribution on the circle, we assume that the original distribution was distributed on the real line, and then wrapped onto the circle. If we can "unwrap" the distribution off the circle and obtain a distribution on the real line, then the standard statistical techniques for data on the real line can be used. Our proposed methods are flexible and computationally efficient to fit a wide class of wrapped distributions. Furthermore, we can easily compute the usual summary statistics. We present extensive simulation studies to validate the performance of our method. We apply our method to several real data sets and compare our results to parameter estimates available in the literature. We find that the Wrapped Double Exponential family produces robust parameter estimates with good frequentist coverage probability. We extend our method to the regression model. As an example, we analyze the association between ozone data and wind direction. A major contribution of this dissertation is to illustrate a technique to interpret the circular regression coefficients in terms of the linear regression model setup. Regression diagnostics can be developed after augmenting wrapping numbers to the circular data (refer Section 3.5). We extend our method to fit time-correlated data. We can compute other statistics such as circular autocorrelation functions and their standard errors very easily. We use the Wrapped Normal model to analyze the hourly wind directions, which is an example of the time series circular data.

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White, Gentry. "Bayesian semiparametric spatial and joint spatio-temporal modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4450.

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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 2, 2007) Vita. Includes bibliographical references.

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Billig, Ian A. "Bayesian Analysis of Systematic Theoretical Errors Models." Ohio University Honors Tutorial College / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors155619979679762.

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Kodi, Ramanah Doogesh. "Bayesian statistical inference and deep learning for primordial cosmology and cosmic acceleration." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS169.

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Cette thèse a pour vocation le développement et l’application de nouvelles techniques d’inférence statistique bayésienne et d’apprentissage profond pour relever les défis statistiques imposés par les gros volumes de données complexes des missions du fond diffus cosmologique (CMB) ou des relevés profonds de galaxies de la prochaine génération, dans le but d'optimiser l’exploitation des données scientifiques afin d’améliorer, à terme, notre compréhension de l’Univers. La première partie de cette thèse concerne l'extraction des modes E et B du signal de polarisation du CMB à partir des données. Nous avons développé une méthode hiérarchique à haute performance, nommée algorithme du dual messenger, pour la reconstruction du champ de spin sur la sphère et nous avons démontré les capacités de cet algorithme à reconstruire des cartes E et B pures, tout en tenant compte des modèles de bruit réalistes. La seconde partie porte sur le développement d’un cadre d'inférence bayésienne pour contraindre les paramètres cosmologiques en s’appuyant sur une nouvelle implémentation du test géométrique d'Alcock-Paczyński et nous avons présenté nos contraintes cosmologiques sur la densité de matière et l'équation d'état de l'énergie sombre. Etant donné que le contrôle des effets systématiques est un facteur crucial, nous avons également présenté une fonction de vraisemblance robuste, qui résiste aux contaminations inconnues liées aux avant-plans. Finalement, dans le but de construire des émulateurs de dynamiques complexes dans notre modèle, nous avons conçu un nouveau réseau de neurones qui apprend à peindre des distributions de halo sur des champs approximatifs de matière noire en 3D
The essence of this doctoral research constitutes the development and application of novel Bayesian statistical inference and deep learning techniques to meet statistical challenges of massive and complex data sets from next-generation cosmic microwave background (CMB) missions or galaxy surveys and optimize their scientific returns to ultimately improve our understanding of the Universe. The first theme deals with the extraction of the E and B modes of the CMB polarization signal from the data. We have developed a high-performance hierarchical method, known as the dual messenger algorithm, for spin field reconstruction on the sphere and demonstrated its capabilities in reconstructing pure E and B maps, while accounting for complex and realistic noise models. The second theme lies in the development of various aspects of Bayesian forward modelling machinery for optimal exploitation of state-of-the-art galaxy redshift surveys. We have developed a large-scale Bayesian inference framework to constrain cosmological parameters via a novel implementation of the Alcock-Paczyński test and showcased our cosmological constraints on the matter density and dark energy equation of state. With the control of systematic effects being a crucial limiting factor for modern galaxy redshift surveys, we also presented an augmented likelihood which is robust to unknown foreground and target contaminations. Finally, with a view to building fast complex dynamics emulators in our above Bayesian hierarchical model, we have designed a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions

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Michell, Justin Walter. "A review of generalized linear models for count data with emphasis on current geospatial procedures." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019989.

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Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.

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