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Relevant bibliographies by topics / Multivariate Markov chains

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Academic literature on the topic 'Multivariate Markov chains'

Author: Grafiati

Published: 10 September 2021

Last updated: 17 July 2025

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Journal articles on the topic "Multivariate Markov chains"

1

Flournoy, Nancy. "Dependency in multivariate markov chains." Linear Algebra and its Applications 127 (1990): 85–106. http://dx.doi.org/10.1016/0024-3795(90)90337-c.

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Janssen, A., and J. Segers. "Markov Tail Chains." Journal of Applied Probability 51, no. 4 (2014): 1133–53. http://dx.doi.org/10.1239/jap/1421763332.

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The extremes of a univariate Markov chain with regularly varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random walk structure called the tail chain. In this paper we extend this fact to Markov chains with multivariate regularly varying marginal distributions inRd. We analyze both the forward and the backward tail process and show that they mutually determine each other through a kind of adjoint relation. In a broader setting, we will show that even for non-Markovian underlying processes a Markovian forward tail chain always imp

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Janssen, A., and J. Segers. "Markov Tail Chains." Journal of Applied Probability 51, no. 04 (2014): 1133–53. http://dx.doi.org/10.1017/s0001867800012027.

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The extremes of a univariate Markov chain with regularly varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random walk structure called the tail chain. In this paper we extend this fact to Markov chains with multivariate regularly varying marginal distributions in R d . We analyze both the forward and the backward tail process and show that they mutually determine each other through a kind of adjoint relation. In a broader setting, we will show that even for non-Markovian underlying processes a Markovian forward tail chain always

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Janssen, A., and J. Segers. "Markov Tail Chains." Journal of Applied Probability 51, no. 04 (2014): 1133–53. http://dx.doi.org/10.1017/s002190020001202x.

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The extremes of a univariate Markov chain with regularly varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random walk structure called the tail chain. In this paper we extend this fact to Markov chains with multivariate regularly varying marginal distributions in R d . We analyze both the forward and the backward tail process and show that they mutually determine each other through a kind of adjoint relation. In a broader setting, we will show that even for non-Markovian underlying processes a Markovian forward tail chain always

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Colombi, R., and S. Giordano. "Graphical models for multivariate Markov chains." Journal of Multivariate Analysis 107 (May 2012): 90–103. http://dx.doi.org/10.1016/j.jmva.2012.01.010.

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Lee, Shiowjen, and J. Lynch. "Total Positivity of Markov Chains and the Failure Rate Character of Some First Passage Times." Advances in Applied Probability 29, no. 3 (1997): 713–32. http://dx.doi.org/10.2307/1428083.

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It is shown that totally positive order 2 (TP2) properties of the infinitesimal generator of a continuous-time Markov chain with totally ordered state space carry over to the chain's transition distribution function. For chains with such properties, failure rate characteristics of the first passage times are established. For Markov chains with partially ordered state space, it is shown that the first passage times have an IFR distribution under a multivariate total positivity condition on the transition function.

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Lee, Shiowjen, and J. Lynch. "Total Positivity of Markov Chains and the Failure Rate Character of Some First Passage Times." Advances in Applied Probability 29, no. 03 (1997): 713–32. http://dx.doi.org/10.1017/s0001867800028317.

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It is shown that totally positive order 2 (TP2) properties of the infinitesimal generator of a continuous-time Markov chain with totally ordered state space carry over to the chain's transition distribution function. For chains with such properties, failure rate characteristics of the first passage times are established. For Markov chains with partially ordered state space, it is shown that the first passage times have an IFR distribution under a multivariate total positivity condition on the transition function.

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Nicolau, João. "A New Model for Multivariate Markov Chains." Scandinavian Journal of Statistics 41, no. 4 (2014): 1124–35. http://dx.doi.org/10.1111/sjos.12087.

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Nicolau, João, and Flavio Ivo Riedlinger. "Estimation and inference in multivariate Markov chains." Statistical Papers 56, no. 4 (2014): 1163–73. http://dx.doi.org/10.1007/s00362-014-0630-6.

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Perfekt, Roland. "Extreme Value Theory for a Class of Markov Chains with Values in ℝd". Advances in Applied Probability 29, № 1 (1997): 138–64. http://dx.doi.org/10.2307/1427864.

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We consider extreme value theory for a class of stationary Markov chains with values in ℝd. The asymptotic distribution of Mn, the vector of componentwise maxima, is determined under mild dependence restrictions and suitable assumptions on the marginal distribution and the transition probabilities of the chain. This is achieved through computation of a multivariate extremal index of the sequence, extending results of Smith [26] and Perfekt [21] to a multivariate setting. As a by-product, we obtain results on extremes of higher-order, real-valued Markov chains. The results are applied to a freq

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Dissertations / Theses on the topic "Multivariate Markov chains"

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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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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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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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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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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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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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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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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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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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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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Books on the topic "Multivariate Markov chains"

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Schwartz, Brian. Therapeuteneffekte Auf Outcome, Sitzungsanzahl und Dropout: Multivariate Multilevel-Analyse Mit Markov-Chain-Monte-Carlo-Schätzung. Springer Fachmedien Wiesbaden GmbH, 2016.

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Boudreau, Joseph F., and Eric S. Swanson. Monte Carlo methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0007.

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Monte Carlo methods are those designed to obtain numerical answers with the use of random numbers . This chapter discusses random engines, which provide a pseudo-random pattern of bits, and their use in for sampling a variety of nonuniform distributions, for both continuous and discrete variables. A wide selection of uniform and nonuniform variate generators from the C++ standard library are reviewed, and common techniques for generating custom nonuniform variates are discussed. The chapter presents the uses of Monte Carlo to evaluate integrals, particularly multidimensional integrals, and the

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Algebraic Statistics. American Mathematical Society, 2018.

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Book chapters on the topic "Multivariate Markov chains"

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Ching, Wai-Ki, Ximin Huang, Michael K. Ng, and Tak-Kuen Siu. "Multivariate Markov Chains." In International Series in Operations Research & Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6312-2_7.

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Sutter, David. "Multivariate Trace Inequalities." In Approximate Quantum Markov Chains. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78732-9_4.

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Colombi, Roberto, and Sabrina Giordano. "Monotone Graphical Multivariate Markov Chains." In Proceedings of COMPSTAT'2010. Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_43.

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Hansen, Peter Reinhard, Guillaume Horel, Asger Lunde, and Ilya Archakov. "A Markov Chain Estimator of Multivariate Volatility from High Frequency Data." In The Fascination of Probability, Statistics and their Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25826-3_17.

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Guo, Shuai, Chao Liu, Zhongwen Guo, Yuan Feng, Feng Hong, and Haiguang Huang. "Trajectory Prediction for Ocean Vessels Base on K-order Multivariate Markov Chain." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94268-1_12.

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Bhangale, Aditi M., and Terrence D. Jorgensen. "Comparing Maximum Likelihood to Markov Chain Monte Carlo Estimation of the Multivariate Social Relations Model." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55548-0_7.

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Csenki, Attila. "The Number of Visits Until Absorption to Subsets of the State Space by a Discrete-Parameter Markov Chain: the Multivariate Case." In Dependability for Systems with a Partitioned State Space. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2674-1_3.

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Di Maso, Matteo, Monica Ferraroni, Pasquale Ferrante, Serena Delbue, and Federico Ambrogi. "Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.36.

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In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years.

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"Consistency of Finite Multivariate Markov Chains." In Structured Dependence between Stochastic Processes. Cambridge University Press, 2020. http://dx.doi.org/10.1017/9781316650530.003.

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"Consistency of Finite Multivariate Conditional Markov Chains." In Structured Dependence between Stochastic Processes. Cambridge University Press, 2020. http://dx.doi.org/10.1017/9781316650530.004.

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Conference papers on the topic "Multivariate Markov chains"

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García, Jesús E. "Combining multivariate Markov chains." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912373.

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Monbet, Vale´rie, Pierre Ailliot, and Marc Prevosto. "Nonlinear Simulation of Multivariate Sea State Time Series." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67490.

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In this paper, three nonlinear methods are described for artificially generating operational sea state histories. In the first method, referred to as Translated Gaussian Process the observed time series is transformed to a process which is supposed to be Gaussian. This Gaussian process is simulated and back transformed. The second method, called Local Grid Bootstrap, consists in a resampling algorithm for Markov chains within which the transition probabilities are estimated locally. The last models is a Markov Switching Autoregressive model which allows in particular to model different weather

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Wang, Zhilong, Zengtai Gong, Weigang Zhao, and Wenjin Zhu. "Higher-Order Multivariate Markov Chains Based on Particle Swarm Optimization Algorithm for Air Pollution Forecasting." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.19.

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Llanes, Jose Damian, Alejo Viñales, and Juan Juri. "Assisted 3D Model Construction and Facies Propagation in Golfo San Jorge Basin Reservoirs for Modelling EOR." In SPE Improved Oil Recovery Conference. SPE, 2022. http://dx.doi.org/10.2118/209400-ms.

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Abstract Three-dimensional modelling is at the critical path to map the by-passed oil in multilayer fluvial systems in the San Jorge Basin. Integrated reservoir modelling teams dedicate an important amount of time to create these three-dimensional models to decrease risk pursuing chemical injection for enhanced oil recovery. Traditional static reservoir modelling requires an important effort from the geologist to construct the interwell correlation. The objective of this work is to show the implementation of two unsupervised algorithms to automate/assist integrated reservoir modelling. We crea

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Song, Na, Wai-Ki Ching, Tak-Kuen Siu, Eric S. Fung, and Michael K. Ng. "Option Valuation under a Multivariate Markov Chain Model." In 2010 Third International Joint Conference on Computational Science and Optimization. IEEE, 2010. http://dx.doi.org/10.1109/cso.2010.73.

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Vaiciulyte, Ingrida. "Adaptive Monte-Carlo Markov chain for multivariate statistical estimation." In International Workshop of "Stochastic Programming for Implementation and Advanced Applications". The Association of Lithuanian Serials, 2012. http://dx.doi.org/10.5200/stoprog.2012.21.

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The estimation of the multivariate skew t-distribution by the Monte-Carlo Markov Chain (MCMC) method is considered in the paper. Thus, the MCMC procedure is constructed for recurrent estimation of skew t-distribution, following the maximum likelihood method, where the Monte-Carlo sample size is regulated to ensure the convergence and to decrease the total amount of Monte-Carlo trials, required for estimation. The confidence intervals of Monte-Carlo estimators are introduced because of their asymptotic normality. The termination rule is also implemented by testing statistical hypotheses on an i

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Gjika (Dhamo), Eralda, Lule Basha, Xhensilda Allka, and Aurora Ferrja. "Predicting the Albanian economic development using multivariate Markov chain model." In 11th International Scientific Conference „Business and Management 2020“. VGTU Technika, 2020. http://dx.doi.org/10.3846/bm.2020.581.

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In this work, the economic development and relation to social and demography indices in Albania were studied. Four time series (yearly data for the period 1995–2018) were considered: consumer price index (CPI), unemployment rate, inflation and life expectancy. In our approach, a first and fifth order multivariate Markov chain model was proposed to predict the economic situation in Albania in the proceedings years. Tests and accuracy analysis of the model were performed. The prediction probabilities fall in the interval of 0.47 to 0.52 and the accuracy of both models is 75%. Our approach is a s

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Viola, M. L. Lanfredi, and Jesús E. García. "Independence’s partition of the set of coordinates of a multivariate Markov chain." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: ICNAAM2022. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0211060.

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Kou, Liu, Chuan-Sheng Yang, and Chao Wang. "A Tridiagonal Parsimonious Higher Order Multivariate Markov Chain Model with New Convergence Condition." In 2017 7th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icadme-17.2017.54.

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Zhu, Dong-Mei, and Wai-Ki Ching. "A New Estimation Method for Multivariate Markov Chain Model with Application in Demand Predictions." In 2010 3rd International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2010. http://dx.doi.org/10.1109/bife.2010.39.

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