Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Athanasios, Kottas"'
Autor:
Matthew Heiner, Athanasios Kottas
Publikováno v:
Journal of Computational and Graphical Statistics. 31:100-112
We develop two models for Bayesian estimation and selection in high-order, discrete-state Markov chains. Both are based on the mixture transition distribution, which constructs a transition probability tensor with additive mixing of probabilities fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6b4b23c57ccd22c45f0f21109218128
https://doi.org/10.1080/10618600.2021.1979565
https://doi.org/10.1080/10618600.2021.1979565
Publikováno v:
Journal of Computational and Graphical Statistics. 31:283-293
Mixture transition distribution time series models build high-order dependence through a weighted combination of first-order transition densities for each one of a specified number of lags. We present a framework to construct stationary transition mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44496a6c7ff1ae49512127c95eb4a7d2
https://doi.org/10.1080/10618600.2021.1981342
https://doi.org/10.1080/10618600.2021.1981342
Publikováno v:
Journal of the Royal Statistical Society Series B: Statistical Methodology. 82:1371-1392
Summary An integro-difference equation can be represented as a hierarchical spatiotemporal dynamic model using appropriate parameterizations. The dynamics of the process defined by an integro-difference equation depends on the choice of a bivariate k
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a34e2d8fb9c7218ddc5cc846b559971
https://doi.org/10.1111/rssb.12393
https://doi.org/10.1111/rssb.12393
Publikováno v:
Technometrics. 63:100-115
We propose a flexible approach to modeling for renewal processes. The model is built from a structured mixture of Erlang densities for the renewal process inter-arrival density. The Erlang mixture ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59123ce438b0a644b42b1609a8d87a66
https://doi.org/10.1080/00401706.2019.1693428
https://doi.org/10.1080/00401706.2019.1693428
Autor:
Athanasios Kottas, Hyotae Kim
Publikováno v:
Statistics and Computing. 32
We develop a prior probability model for temporal Poisson process intensities through structured mixtures of Erlang densities with common scale parameter, mixing on the integer shape parameters. The mixture weights are constructed through increments
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1f2088b9038263315a6cb5ec44306ed
https://doi.org/10.1007/s11222-021-10064-0
https://doi.org/10.1007/s11222-021-10064-0
Publikováno v:
Statistics and Computing. 29:1077-1093
We develop two prior distributions for probability vectors which, in contrast to the popular Dirichlet distribution, retain sparsity properties in the presence of data. Our models are appropriate for count data with many categories, most of which are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a3a64bf73880f75f4dca69448a905c3
https://doi.org/10.1007/s11222-019-09856-2
https://doi.org/10.1007/s11222-019-09856-2
Autor:
Athanasios Kottas, Matthew Heiner
We develop a mixture model for transition density approximation, together with soft model selection, in the presence of noisy and heterogeneous nonlinear dynamics. Our model builds on the Gaussian mixture transition distribution (MTD) model for conti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3da1a760e96aeb6749846ee623bd5097
http://arxiv.org/abs/2007.09279
http://arxiv.org/abs/2007.09279
Autor:
Athanasios Kottas, Maria DeYoreo
We present a Bayesian nonparametric model for ordinal responses, which is based on mixture modelling for the joint distribution of covariates and latent continuous responses. The modelling framework enables flexible inference for both the regression
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::396038319a90a86b9be2c9ff48cf82e2
https://doi.org/10.1016/b978-0-12-815862-3.00009-3
https://doi.org/10.1016/b978-0-12-815862-3.00009-3
Autor:
Taeryon Choi, James S. Clark, Erika Cunningham, Maria DeYoreo, J.-L. Dortet-Bernadet, Y. Fan, Michele Guindani, Maria Kalli, Nadja Klein, Thomas Kneib, Jeong Hwan Kook, Athanasios Kottas, Peter J. Lenk, Yadong Lu, Andrew A. Manderson, Giampiero Marra, Yinsen Miao, Kevin Murray, John T. Ormerod, Rosalba Radice, T. Rodrigues, Surya T. Tokdar, Berwin A. Turlach, Marina Vannucci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91889a69dac818ca1501e89b40ba13e1
https://doi.org/10.1016/b978-0-12-815862-3.00005-6
https://doi.org/10.1016/b978-0-12-815862-3.00005-6
Autor:
Athanasios Kottas
Publikováno v:
Statistical Methods & Applications. 27:219-225
This is an invited discussion of review paper “Nonparametric Bayesian Inference in Applications” by Peter Muller, Fernando A. Quintana and Garritt L. Page.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d99c719cd1db1bfa704d72087c455b62
https://doi.org/10.1007/s10260-017-0398-7
https://doi.org/10.1007/s10260-017-0398-7