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Relevant bibliographies by topics / Multivariate Markov chains / Dissertations / Theses

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Dissertations / Theses on the topic 'Multivariate Markov chains'

Author: Grafiati

Published: 10 September 2021

Last updated: 29 July 2025

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1

Zhou, Hua. "Examples of multivariate Markov chains with orthogonal polynomial eigenfunctions /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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2

Damásio, Bruno Miguel Pinto. "Multivariate Markov Chains - estimation, inference and forecast. A new approach : what if we use them as stochastic covariates?" Master's thesis, Instituto Superior de Economia e Gestão, 2013. http://hdl.handle.net/10400.5/6397.

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Mestrado em Econometria Aplicada e Previsão<br>This dissertation proposes a new concept: the usage of Multivariate Markov Chains (MMC) as covariates. Our innovative approach is based on the observation that we can treat possible categorical regressors as a MMC in order to improve the forecast error of a certain dependent variable,provided it is caused, in the Granger sense, by the MMC. We conduct a Monte Carlo simulation study to assess the performance of our model and we archive excellent results in terms of forecast. An empirical illustration, that widely supports the results obtained in t

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3

Yildirak, Sahap Kasirga. "The Identificaton Of A Bivariate Markov Chain Market Model." Phd thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/1257898/index.pdf.

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This work is an extension of the classical Cox-Ross-Rubinstein discrete time market model in which only one risky asset is considered. We introduce another risky asset into the model. Moreover, the random structure of the asset price sequence is generated by bivariate finite state Markov chain. Then, the interest rate varies over time as it is the function of generating sequences. We discuss how the model can be adapted to the real data. Finally, we illustrate sample implementations to give a better idea about the use of the model.

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4

Karawatzki, Roman, and Josef Leydold. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/294/1/document.pdf.

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Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to "conventional methods", especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms method. We show that it is well suited for an algorithm to generate points from quite arbitrary distributions, which include all log-concave distributions. The algorithm works automatically in the sense that

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5

Karawatzki, Roman, Josef Leydold, and Klaus Pötzelberger. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1400/1/document.pdf.

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Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to "conventional methods", especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms method. We show that it is well suited for an algorithm to generate points from quite arbitrary distributions, which include all log-concave distributions. The algorithm works automatically in the sense that

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6

Song, Joon Jin. "Bayesian multivariate spatial models and their applications." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1122.

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Univariate hierarchical Bayes models are being vigorously researched for use in disease mapping, engineering, geology, and ecology. This dissertation shows how the models can also be used to build modelbased risk maps for areabased roadway traﬃc crashes. Countylevel vehicle crash records and roadway data from Texas are used to illustrate the method. A potential extension that uses univariate hierarchical models to develop networkbased risk maps is also discussed. Several Bayesian multivariate spatial models for estimating the traﬃc crash rates from diﬀerent types of crash

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7

LUCCHESE, Gianfranco. "Multivariate hedonic models for heterogeneous product prices in dynamic supply chains." Doctoral thesis, Università degli studi di Bergamo, 2012. http://hdl.handle.net/10446/26713.

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Identifying parameters for state-space models in high dimensioned cases requires a complex methodology. We offer an example of application for hedonic prices and the hyper-parameter estimation for dynamic supply chains. An algorithm is created based on the Kalman filter-smoother and Expectation-Maximization procerures. Stopping rules for the algorithm are analyzed and compared. We detected the best stopping rule for our environment. In this way, the hedonic prices estimated can be used for any decision process. The thesis point to an application in forecast analysis for product prices. Accura

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8

Fu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.

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The analysis of big data is of great interest today, and this comes with challenges of improving precision and efficiency in estimation and prediction. We study binary data with covariates from numerous small areas, where direct estimation is not reliable, and there is a need to borrow strength from the ensemble. This is generally done using Bayesian logistic regression, but because there are numerous small areas, the exact computation for the logistic regression model becomes challenging. Therefore, we develop an integrated multivariate normal approximation (IMNA) method for binary data with

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9

Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4875/1/research_report_updated.pdf.

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We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almo

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10

Hudson, Brent. "Modelling the Covariance Dynamics of Multivariate Financial Time Series." Thesis, The University of Sydney, 2011. http://hdl.handle.net/2123/8086.

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Investor performance in financial markets can be significantly affected by their ability to model market volatility and correlation over time. The effectiveness of various market activities such as option pricing, portfolio optimisation and risk management rely on the accuracy of such modelling. This thesis proposes a series of multivariate GARCH models that attempt to accurately capture the volatility and correlation dynamics of stock returns. A Bayesian approach is utilised to estimate model parameters, extending classical maximum likelihood (ML) approaches commonly used in the literature f

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11

Yalcinoz, Zerrin. "A Simulation Study On Marginalized Transition Random Effects Models For Multivariate Longitudinal Binary Data." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609568/index.pdf.

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In this thesis, a simulation study is held and a statistical model is fitted to the simulated data. This data is assumed to be the satisfaction of the customers who withdraw their salary from a particular bank. It is a longitudinal data which has bivariate and binary response. It is assumed to be collected from 200 individuals at four different time points. In such data sets, two types of dependence -the dependence within subject measurements and the dependence between responses- are important and these are considered in the model. The model is Marginalized Transition Random Effects Models, wh

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12

Harlow, Jennifer. "Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference." Thesis, University of Canterbury. School of Mathematics and Statistics, 2013. http://hdl.handle.net/10092/9160.

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A regular paving (RP) is a finite succession of bisections that partitions a multidimensional box into sub-boxes using a binary tree-based data structure, with the restriction that an existing sub-box in the partition may only be bisected on its first widest side. Mapping a real value to each element of the partition gives a real-mapped regular paving (RMRP) that can be used to represent a piecewise-constant function density estimate on a multidimensional domain. The RP structure allows real arithmetic to be extended to density estimates represented as RMRPs. Other operations such as computi

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13

Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Bayesian Inference in the Multinomial Logit Model." Austrian Statistical Society, 2012. http://epub.wu.ac.at/5629/1/186%2D751%2D1%2DSM.pdf.

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The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.

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14

Liu, Yi. "Time-Varying Coefficient Models for Recurrent Events." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/97999.

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I have developed time-varying coefficient models for recurrent event data to evaluate the temporal profiles for recurrence rate and covariate effects. There are three major parts in this dissertation. The first two parts propose a mixed Poisson process model with gamma frailties for single type recurrent events. The third part proposes a Bayesian joint model based on multivariate log-normal frailties for multi-type recurrent events. In the first part, I propose an approach based on penalized B-splines to obtain smooth estimation for both time-varying coefficients and the log baseline intensity

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15

AZZILEY, AZZIBROUCK GEORGES. "Sedimentologie et geochimie du francevillien b (proterozoique inferieur). Metallogenie des gisements de manganese de moanda, gabon." Université Louis Pasteur (Strasbourg) (1971-2008), 1986. http://www.theses.fr/1986STR13041.

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16

Assareh, Hassan. "Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53342/1/Hassan_Assareh_Thesis.pdf.

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Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular

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17

Kuhnert, Petra Meta. "New methodology and comparisons for the analysis of binary data using Bayesian and tree based methods." Thesis, Queensland University of Technology, 2003.

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18

Damásio, Bruno. "Essays on econometrics. Multivariate Markov chains." Doctoral thesis, 2018. http://hdl.handle.net/10400.5/18128.

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Tese de Doutoramento em Matemática aplicada à Economia e Gestão<br>This Dissertation is about Markov chains and their role in economics and in econometrics theory. Four essays on the Markov chain ap- proach are presented. We start by illustrating the analytical potential of multivariate Markov chains in the field of economic history, in particular with regard to a test of the Schumpeterian hypothesis of creative destruction. Then, we ilustrate the flexibility of Markov chains, and their per- tinence to situations that go beyond their traditional applicability: i) how can a Markov chain

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19

"Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, 2005. http://epub.wu-wien.ac.at/dyn/dl/wp/epub-wu-01_8cb.

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20

Thompson, Madeleine. "Slice Sampling with Multivariate Steps." Thesis, 2011. http://hdl.handle.net/1807/31955.

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Markov chain Monte Carlo (MCMC) allows statisticians to sample from a wide variety of multidimensional probability distributions. Unfortunately, MCMC is often difficult to use when components of the target distribution are highly correlated or have disparate variances. This thesis presents three results that attempt to address this problem. First, it demonstrates a means for graphical comparison of MCMC methods, which allows researchers to compare the behavior of a variety of samplers on a variety of distributions. Second, it presents a collection of new slice-sampling MCMC methods. These meth

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21

Raffa, Jesse Daniel. "Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models." Thesis, 2012. http://hdl.handle.net/10012/7255.

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Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal response can be attributed to underlying unobserved disease states in addition to any between-subject differences. We propose modeling such disease states using a hidden Markov model (HMM) approach and expand upon previous work, which incorporated random effects into HMMs for the analysis of univariate longitudinal data, to the setting of a multivariate longitudinal response. Multivariate

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22

Hassan, Zadeh Amin. "Actuarial applications of multivariate phase-type distributions : model calibration and credibility." Thèse, 2009. http://hdl.handle.net/1866/6540.

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23

Uzeda, Garcia Luis Henrique. "Bayesian Inference of State Space Models with Flexible Covariance Matrix Rank: Applications for Inflation Modeling." Phd thesis, 2017. http://hdl.handle.net/1885/118192.

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After the introductory chapter, this thesis comprises two further chapters. The main chapters in this dissertation, i.e., chapters 2 and 3 are presented in essay format, each with an independent introduction and conclusion. The contents of these individual chapters are outlined below. Chapter 2 studies the forecasting implications of specifying unobserved components (UC) models with different state correlation structures. While implications for signal extraction from specifying UC models with correlated or orthogonal innovations have been well-inves

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24

Mondal, Anirban. "Bayesian Uncertainty Quantification for Large Scale Spatial Inverse Problems." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9905.

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We considered a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a high dimension spatial field. The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provides a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. Karhunen-Lo'eve expansion and Discrete Cosine transform were used for dimension reduction of the random spatial field.

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