Academic literature on the topic 'Multilevel models (Statistics) Markov processes'
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Journal articles on the topic "Multilevel models (Statistics) Markov processes"
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.
Full textHuzurbazar, 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.
Full textDIDELEZ, 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.
Full textMitrophanov, 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.
Full textResnick, 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.
Full textBartolucci, 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.
Full textŌsawa, 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.
Full textLefèvre, 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.
Full textAggoun, 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.
Full textBorisov, 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.
Full textDissertations / Theses on the topic "Multilevel models (Statistics) Markov processes"
Arab, Ali. "Hierarchical spatio-temporal models for environmental processes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4698.
Full textThe 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.
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.
Full textDrton, 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.
Full textMuller, 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.
Full textENGLISH 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.
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.
Full textMujumdar, 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.
Full textHo, 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.
Full textGuha, Subharup. "Benchmark estimation for Markov Chain Monte Carlo samplers." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085594208.
Full textLeung, 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.
Full textKim, Yong Ku. "Bayesian multiresolution dynamic models." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180465799.
Full textBooks on the topic "Multilevel models (Statistics) Markov processes"
author, Farcomeni Alessio, and Pennoni Fulvia author, eds. Latent Markov models for longitudinal data. Boca Raton: CRC Press, 2013.
Find full textChang-Jin, Kim. Estimation of Markov regime-switching regression models with endogenous switching. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.
Find full textKoroli͡uk, V. S. Stochastic models of systems. Dordrecht: Kluwer Academic Publishers, 1999.
Find full textZucchini, W. Hidden Markov models for time series: An introduction using R. Boca Raton: Chapman & Hall/CRC, 2009.
Find full textM. N. M. van Lieshout. Stochastic geometry models in image analysis and spatial statistics. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1995.
Find full textLim, Kian Guan. Probability and finance theory. New Jersey: World Scientific, 2011.
Find full textIribarren, 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.
Find full textJacqueline, Gianini, ed. Modèles probabilistes d'aide à la décision. Sillery, Québec: Presses de l'Université du Québec, 1987.
Find full textHaccou, Patsy. Statistical analysis of behavioural data: An approach based on time-structured models. Oxford: Oxford University Press, 1992.
Find full textBook chapters on the topic "Multilevel models (Statistics) Markov processes"
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.
Full textRudnicki, 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.
Full textConference papers on the topic "Multilevel models (Statistics) Markov processes"
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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