Relevant bibliographies by topics / Multilevel models (Statistics) Markov processes

Academic literature on the topic 'Multilevel models (Statistics) Markov processes'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Multilevel models (Statistics) Markov processes":

1

Keiding, Niels, and Richard D. Gill. "Random Truncation Models and Markov Processes." Annals of Statistics 18, no. 2 (June 1990): 582–602. http://dx.doi.org/10.1214/aos/1176347617.

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Huzurbazar, Aparna V. "Multistate Models, Flowgraph Models, and Semi-Markov Processes." Communications in Statistics - Theory and Methods 33, no. 3 (January 5, 2004): 457–74. http://dx.doi.org/10.1081/sta-120028678.

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DIDELEZ, VANESSA. "Graphical Models for Composable Finite Markov Processes." Scandinavian Journal of Statistics 34, no. 1 (March 2007): 169–85. http://dx.doi.org/10.1111/j.1467-9469.2006.00528.x.

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Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 3 (September 2005): 632–42. http://dx.doi.org/10.1239/jap/1127322017.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.
5

Resnick, Sidney, and Rishin Roy. "Multivariate extremal processes, leader processes and dynamic choice models." Advances in Applied Probability 22, no. 2 (June 1990): 309–31. http://dx.doi.org/10.2307/1427538.

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Let (Y(t), t > 0) be a d-dimensional non-homogeneous multivariate extremal process. We suppose the ith component of Y describes time-dependent behaviour of random utilities associated with the ith choice. At time t we choose the ith alternative if the ith component of Y(t) is the largest of all the components. Let J(t) be the index of the largest component at time t so J has range {1, …, d} and call {J(t)} the leader process. Let Z(t) be the value of the largest component at time t. Then the bivariate process (J(t), Z(t)} is Markov. We discuss when J(t) and Z(t) are independent, when {J(s), 0<s≦t} and Z(t) are independent and when J(t) and {Z(s), 0<s≦t} are independent. In usual circumstances, {J(t)} is Markov and particular properties are given when the underlying distribution is max-stable. In the max-stable time-homogeneous case, {J(et)} is a stationary Markov chain with stationary transition probabilities.
6

Bartolucci, Francesco, and Monia Lupparelli. "Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data." Journal of the American Statistical Association 111, no. 513 (January 2, 2016): 216–28. http://dx.doi.org/10.1080/01621459.2014.998935.

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Ōsawa, Hideo. "Reversibility of Markov chains with applications to storage models." Journal of Applied Probability 22, no. 1 (March 1985): 123–37. http://dx.doi.org/10.2307/3213752.

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This paper studies the reversibility conditions of stationary Markov chains (discrete-time Markov processes) with general state space. In particular, we investigate the Markov chains having atomic points in the state space. Such processes are often seen in storage models, for example waiting time in a queue, insurance risk reserve, dam content and so on. The necessary and sufficient conditions for reversibility of these processes are obtained. Further, we apply these conditions to some storage models and present some interesting results for single-server queues and a finite insurance risk model.
8

Lefèvre, Claude, and Matthieu Simon. "SIR-Type Epidemic Models as Block-Structured Markov Processes." Methodology and Computing in Applied Probability 22, no. 2 (April 3, 2019): 433–53. http://dx.doi.org/10.1007/s11009-019-09710-y.

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Aggoun, Lakhdar, and Robert J. Elliott. "Finite-dimensional models for hidden Markov chains." Advances in Applied Probability 27, no. 1 (March 1995): 146–60. http://dx.doi.org/10.2307/1428101.

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A continuous-time, non-linear filtering problem is considered in which both signal and observation processes are Markov chains. New finite-dimensional filters and smoothers are obtained for the state of the signal, for the number of jumps from one state to another, for the occupation time in any state of the signal, and for joint occupation times of the two processes. These estimates are then used in the expectation maximization algorithm to improve the parameters in the model. Consequently, our filters and model are adaptive, or self-tuning.
10

Borisov, A. V. "State Analysis of Hidden Markov Models Governed by Special Jump Processes." Theory of Probability & Its Applications 51, no. 3 (January 2007): 518–28. http://dx.doi.org/10.1137/s0040585x97982542.

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You might also be interested in the extended bibliographies on the topic 'Multilevel models (Statistics) Markov processes' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Multilevel models (Statistics) Markov processes":

1

Arab, Ali. "Hierarchical spatio-temporal models for environmental processes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4698.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
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 Nov. 21, 2007). Vita. Includes bibliographical references.
2

Yeo, Sungchil. "On estimation for a combined Markov and semi-Markov model with censoring /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487586889187169.

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Drton, Mathias. "Maximum likelihood estimation in Gaussian AMP chain graph models and Gaussian ancestral graph models /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/8952.

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Muller, Christoffel Joseph Brand. "Bayesian approaches of Markov models embedded in unbalanced panel data." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71910.

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Thesis (PhD)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Multi-state models are used in this dissertation to model panel data, also known as longitudinal or cross-sectional time-series data. These are data sets which include units that are observed across two or more points in time. These models have been used extensively in medical studies where the disease states of patients are recorded over time. A theoretical overview of the current multi-state Markov models when applied to panel data is presented and based on this theory, a simulation procedure is developed to generate panel data sets for given Markov models. Through the use of this procedure a simulation study is undertaken to investigate the properties of the standard likelihood approach when fitting Markov models and then to assess its shortcomings. One of the main shortcomings highlighted by the simulation study, is the unstable estimates obtained by the standard likelihood models, especially when fitted to small data sets. A Bayesian approach is introduced to develop multi-state models that can overcome these unstable estimates by incorporating prior knowledge into the modelling process. Two Bayesian techniques are developed and presented, and their properties are assessed through the use of extensive simulation studies. Firstly, Bayesian multi-state models are developed by specifying prior distributions for the transition rates, constructing a likelihood using standard Markov theory and then obtaining the posterior distributions of the transition rates. A selected few priors are used in these models. Secondly, Bayesian multi-state imputation techniques are presented that make use of suitable prior information to impute missing observations in the panel data sets. Once imputed, standard likelihood-based Markov models are fitted to the imputed data sets to estimate the transition rates. Two different Bayesian imputation techniques are presented. The first approach makes use of the Dirichlet distribution and imputes the unknown states at all time points with missing observations. The second approach uses a Dirichlet process to estimate the time at which a transition occurred between two known observations and then a state is imputed at that estimated transition time. The simulation studies show that these Bayesian methods resulted in more stable results, even when small samples are available.
AFRIKAANSE OPSOMMING: Meerstadium-modelle word in hierdie verhandeling gebruik om paneeldata, ook bekend as longitudinale of deursnee tydreeksdata, te modelleer. Hierdie is datastelle wat eenhede insluit wat oor twee of meer punte in tyd waargeneem word. Hierdie tipe modelle word dikwels in mediese studies gebruik indien verskillende stadiums van ’n siekte oor tyd waargeneem word. ’n Teoretiese oorsig van die huidige meerstadium Markov-modelle toegepas op paneeldata word gegee. Gebaseer op hierdie teorie word ’n simulasieprosedure ontwikkel om paneeldatastelle te simuleer vir gegewe Markov-modelle. Hierdie prosedure word dan gebruik in ’n simulasiestudie om die eienskappe van die standaard aanneemlikheidsbenadering tot die pas vanMarkov modelle te ondersoek en dan enige tekortkominge hieruit te beoordeel. Een van die hoof tekortkominge wat uitgewys word deur die simulasiestudie, is die onstabiele beramings wat verkry word indien dit gepas word op veral klein datastelle. ’n Bayes-benadering tot die modellering van meerstadiumpaneeldata word ontwikkel omhierdie onstabiliteit te oorkom deur a priori-inligting in die modelleringsproses te inkorporeer. Twee Bayes-tegnieke word ontwikkel en aangebied, en hulle eienskappe word ondersoek deur ’n omvattende simulasiestudie. Eerstens word Bayes-meerstadium-modelle ontwikkel deur a priori-verdelings vir die oorgangskoerse te spesifiseer en dan die aanneemlikheidsfunksie te konstrueer deur van standaard Markov-teorie gebruik te maak en die a posteriori-verdelings van die oorgangskoerse te bepaal. ’n Gekose aantal a priori-verdelings word gebruik in hierdie modelle. Tweedens word Bayesmeerstadium invul tegnieke voorgestel wat gebruik maak van a priori-inligting om ontbrekende waardes in die paneeldatastelle in te vul of te imputeer. Nadat die waardes ge-imputeer is, word standaard Markov-modelle gepas op die ge-imputeerde datastel om die oorgangskoerse te beraam. Twee verskillende Bayes-meerstadium imputasie tegnieke word bespreek. Die eerste tegniek maak gebruik van ’n Dirichletverdeling om die ontbrekende stadium te imputeer by alle tydspunte met ’n ontbrekende waarneming. Die tweede benadering gebruik ’n Dirichlet-proses om die oorgangstyd tussen twee waarnemings te beraam en dan die ontbrekende stadium te imputeer op daardie beraamde oorgangstyd. Die simulasiestudies toon dat die Bayes-metodes resultate oplewer wat meer stabiel is, selfs wanneer klein datastelle beskikbaar is.
5

Sans, Fuentes Carles. "Markov Decision Processes and ARIMA models to analyze and predict Ice Hockey player’s performance." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154349.

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In this thesis, player’s performance on ice hockey is modelled to create newmetricsby match and season for players. AD-trees have been used to summarize ice hockey matches using state variables, which combine context and action variables to estimate the impact of each action under that specific state using Markov Decision Processes. With that, an impact measure has been described and four player metrics have been derived by match for regular seasons 2007-2008 and 2008-2009. General analysis has been performed for these metrics and ARIMA models have been used to analyze and predict players performance. The best prediction achieved in the modelling is the mean of the previous matches. The combination of several metrics including the ones created in this thesis could be combined to evaluate player’s performance using salary ranges to indicate whether a player is worth hiring/maintaining/firing
6

Mujumdar, Monali. "Estimation of the number of syllables using hidden Markov models and design of a dysarthria classifier using global statistics of speech." Laramie, Wyo. : University of Wyoming, 2006. http://proquest.umi.com/pqdweb?did=1283963331&sid=6&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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7

Ho, Kwok Wah. "RJMCMC algorithm for multivariate Gaussian mixtures with applications in linear mixed-effects models /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?ISMT%202005%20HO.

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8

Guha, Subharup. "Benchmark estimation for Markov Chain Monte Carlo samplers." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085594208.

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9

Leung, Hiu-lan, and 梁曉蘭. "Wandering ideal point models for single or multi-attribute ranking data: a Bayesian approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29552357.

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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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Books on the topic "Multilevel models (Statistics) Markov processes":

1

Bartolucci, Francesco. Latent Markov models for longitudinal data. Boca Raton: CRC Press, 2013.

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Chang-Jin, Kim. Estimation of Markov regime-switching regression models with endogenous switching. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.

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Koroli͡uk, V. S. Stochastic models of systems. Dordrecht: Kluwer Academic Publishers, 1999.

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Zucchini, W. Hidden Markov models for time series: An introduction using R. Boca Raton: Chapman & Hall/CRC, 2009.

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M. N. M. van Lieshout. Stochastic geometry models in image analysis and spatial statistics. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1995.

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Lim, Kian Guan. Probability and finance theory. New Jersey: World Scientific, 2011.

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Lim, Kian Guan. Probability and finance theory. Hackensack, NJ: World Scientific, 2015.

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Iribarren, Gonzalo Pérez. Cadenas de Markov gobernando algunos procesos aplicables a los ríos: Aplicaciones estadísticas a algunos ríos de la Región. [Montevideo: Publicaciones Matemáticas del Uruguay, 1999.

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Nedzela, Michel. Modèles probabilistes d'aide à la décision. Sillery, Québec: Presses de l'Université du Québec, 1987.

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Haccou, Patsy. Statistical analysis of behavioural data: An approach based on time-structured models. Oxford: Oxford University Press, 1992.

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Book chapters on the topic "Multilevel models (Statistics) Markov processes":

1

Cox, D. R. "Some remarks on semi-Markov processes in medical statistics." In Semi-Markov Models, 411–21. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_24.

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Rudnicki, Ryszard, and Marta Tyran-Kamińska. "Piecewise Deterministic Markov Processes in Biological Models." In Springer Proceedings in Mathematics & Statistics, 235–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12145-1_15.

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Conference papers on the topic "Multilevel models (Statistics) Markov processes":

1

Hasslinger, Gerhard, Anne Schwahn, and Franz Hartleb. "2-State (semi-)Markov processes beyond Gilbert-Elliott: Traffic and channel models based on 2nd order statistics." In IEEE INFOCOM 2013 - IEEE Conference on Computer Communications. IEEE, 2013. http://dx.doi.org/10.1109/infcom.2013.6566938.

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