{"id":1079,"date":"2023-05-04T17:12:36","date_gmt":"2023-05-04T20:12:36","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?page_id=1079"},"modified":"2023-07-19T10:07:48","modified_gmt":"2023-07-19T13:07:48","slug":"publicacoes","status":"publish","type":"page","link":"https:\/\/www.ufrgs.br\/ppgest\/publicacoes\/","title":{"rendered":"Publica\u00e7\u00f5es Selecionadas"},"content":{"rendered":"\n<p>2023<\/p>\n<p>Moresco, Marlon, Marcelo Brutti Righi, and <strong>Eduardo Horta<\/strong>. &#8220;Minkowski deviation measures.&#8221; Statistics &amp; Risk Modeling 40.1-2 (2023): 1-19.<\/p>\n<p><strong>Bayer, F. M<\/strong>.; <strong>Pumi, G.<\/strong>; Pereira, T. L. and Souza, T. C. (2023) Inflated beta autoregressive moving average models. Computational And Applied Mathematics (Forthcoming).<\/p>\n<p>Benaduce, H. S. e <strong>Pumi, G.<\/strong> (2023). SYMARFIMA: a dynamical model for conditionally symmetric time series with long range dependence mean structure. Journal of Statistical Planning and Inference v. 225, p. 71\u201388.<\/p>\n<p>Chen, Y., Torrent H. S., and <strong>Ziegelmann F. A.<\/strong> (forthcoming, 2023+). Robust Nonparametric Frontier Estimation in Two Steps, Econometric Reviews.<\/p>\n<p><strong>Pumi, G.<\/strong>; <strong>Prass, T. S.<\/strong> and Lopes, S. R .C. (2023). A novel copula-based approach for parametric estimation of univariate time series through its covariance decay. Statistical Papers (Forthcoming)<\/p>\n<p>M. Sagrillo, <strong>R. R. Guerra<\/strong>, <strong>F. M. Bayer<\/strong> and R. Machado, &#8220;The LA Distribution: An Approximation of the G0A Distribution for Amplitude SAR Image Modeling,&#8221; in\u00a0<em>IEEE Transactions on Geoscience and Remote Sensing<\/em>, vol. 61, pp. 1-8, 2023, Art no. 5205408, <a href=\"https:\/\/doi.org\/10.1109\/TGRS.2023.3262480\">doi: 10.1109\/TGRS.2023.3262480.<\/a><\/p>\n<p><strong>Taufemback, C.G.<\/strong> (2023), Asymptotic Behavior of Temporal Aggregation in Mixed-Frequency Datasets. Oxford Bulletin of Economics and Statistics, forthcoming, <a href=\"https:\/\/doi.org\/10.1111\/obes.12546\" aria-label=\"Digital Object Identifier\">https:\/\/doi.org\/10.1111\/obes.12546<\/a><\/p>\n<p><strong>Taufemback, C.G.<\/strong> (2023), Non-parametric short- and long-run Granger causality testing in the frequency domain. Journal of Time Series Analysis, 44: 69-92. <a href=\"https:\/\/doi.org\/10.1111\/jtsa.12650\" aria-label=\"Digital Object Identifier\">https:\/\/doi.org\/10.1111\/jtsa.12650<\/a><\/p>\n<p>2022<\/p>\n<p>de Oliveira, B.N.; <strong>Valk, M<\/strong>; <strong>Marcondes Filho, D.<\/strong> 2022. Fault detection and diagnosis of batch process dynamics using ARMA-based control charts. Journal of Process Control. 111:46-58.<\/p>\n<p>GRANDE, A.F. ; <strong>PUMI, G.<\/strong> ; <strong>CYBIS, G. B.<\/strong> . Granger causality and time series regression for modeling the migratory dynamics of Influenza into Brazil. SORT-Statistics and Operations Research Transactions, v. 46, p. 161-188, 2022.<\/p>\n<p>PALM, B. G.; <strong>BAYER, F. M<\/strong>.; CINTRA, R. J.. 2-D Rayleigh autoregressive moving average model for SAR image modeling. Computational Statistics &amp; Data Analysis, v. 171, p. 107453, 2022.<\/p>\n<p><strong>Taufemback, Cleiton G.<\/strong>, Troster, Victor and Shahbaz, Muhammad. &#8220;A Robust Test for Monotonicity in Asset Returns&#8221; <i>Journal of Time Series Econometrics<\/i>, vol. 14, no. 1, 2022, pp. 1-24. <a href=\"https:\/\/doi.org\/10.1515\/jtse-2019-0068\">https:\/\/doi.org\/10.1515\/jtse-2019-0068<\/a><\/p>\n<p>2021<\/p>\n<p>Fernandes, Marcelo, Emmanuel Guerre, and <strong>Eduardo Horta<\/strong>. &#8220;Smoothing quantile regressions.&#8221; Journal of Business &amp; Economic Statistics 39.1 (2021): 338-357.<\/p>\n<p><strong>GUERRA, RENATA ROJAS<\/strong>; PE\u00d1A-RAM\u00cdREZ, FERNANDO A. ; BOURGUIGNON, MARCELO. The unit extended Weibull families of distributions and its applications. JOURNAL OF APPLIED STATISTICS, v. 48, p. 3174-3192, 2021.DOI: <a href=\"https:\/\/doi.org\/10.1080\/02664763.2020.1796936\">https:\/\/doi.org\/10.1080\/02664763.2020.1796936<\/a><\/p>\n<p><strong>Prass, T. S.<\/strong> and <strong>Pumi, G.<\/strong> (2021) On the behavior of the DFA and DCCA in trend-stationary processes. Journal of Multivariate Analysis v. 182, p. 104703.<\/p>\n<p><strong>Pumi. G.<\/strong>; <strong>Prass, T. S.<\/strong> and Souza, R. R. (2021) . A dynamic model for double-bounded time series with chaotic-driven conditional averages. Scandinavian Journal of Statistics, v. 48, p. 68-86.<\/p>\n<p>RIBEIRO, T. F. ; CORDEIRO, G. M. ; PE\u00d1A-RAM\u00cdREZ, F. A. ; <strong>GUERRA, R. R.<\/strong> . A new quantile regression for the COVID-19 mortality rates in the United States. COMPUTATIONAL AND APPLIED MATHEMATICS, v. 40, p. 255, 2021. DOI: <a href=\"https:\/\/doi.org\/10.1007\/s40314-021-01553-z\">https:\/\/doi.org\/10.1007\/s40314-021-01553-z<\/a><\/p>\n<p>Scher, V. T.; Cribari-Neto, F. ; <strong>Pumi, G.<\/strong> and <strong>Bayer, F. M.<\/strong> (2021). Goodness-of-fit tests for \u03b2ARMA hydrological time series modeling. Environmetrics, v. 31, p. env.2607.<\/p>\n<p><strong>Valk, M<\/strong>; <strong>Cybis GB<\/strong>. 2021. U-statistical inference for hierarchical clustering. Journal of Computational and Graphical Statistics. 30(1)<\/p>\n<p>2020<\/p>\n<p><strong>Marcondes, DF<\/strong>; <strong>Valk M.<\/strong> 2020. Dynamic Var Model-Based Control Charts for Batch Process Monitoring. European Journal of Operational Research (EJOR). 285(1):296-305.<\/p>\n<p>MELCHIOR, CRISTIANE ; ZANINI, ROSELAINE RUVIARO ; <strong>GUERRA, RENATA ROJAS<\/strong> ;<br \/>ROCKENBACH, DINEI A. . Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches. INTERNATIONAL JOURNAL OF FORECASTING, v. 37, p. 825-837, 2020. DOI:<a href=\"https:\/\/doi.org\/10.1016\/j.ijforecast.2020.09.010\">https:\/\/doi.org\/10.1016\/j.ijforecast.2020.09.010<\/a><\/p>\n<p><strong>Pumi, G.<\/strong>; Rauber, C. and <strong>Bayer, F. M.<\/strong>(2020). Kumaraswamy regression model with Aranda-Ordaz link function. TEST, v. 29, p. 1051-1071.<\/p>\n<p>2019<\/p>\n<p><strong>Pumi, G.<\/strong>; <strong>Valk, M.<\/strong>; Bisognin, C.; <strong>Bayer, F. M.<\/strong> and <strong>Prass, T. S.<\/strong> (2019). Beta autoregressive fractionally integrated moving average models. Journal of Statistical Planning and Inference, v. 200, 196\u2013212.<\/p>\n<p>2018<\/p>\n<p><strong>Horta, Eduardo<\/strong>, and <strong>Flavio Ziegelmann<\/strong>. &#8220;Dynamics of financial returns densities: A functional approach applied to the Bovespa intraday index.&#8221; International Journal of Forecasting 34.1 (2018): 75-88.<\/p>\n<p><strong>CYBIS, GABRIELA B.<\/strong>;<strong> VALK, MARCIO<\/strong> ; LOPES, S\u00cdLVIA R. C. . Clustering and classification problems in genetics through U -statistics. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v. 88, p. 1882-1902, 2018.<\/p>\n<p><strong>Horta, E.,<\/strong> and <strong>Ziegelmann F. A.<\/strong> (2018). Conjugate processes: Theory and application to risk forecasting, Stochastic Processes and their Applications, Volume 128, p.727-755.<\/p>\n<p>PE\u00d1A-RAM\u00cdREZ, F. A. ; <strong>GUERRA, R. R.<\/strong> ; CORDEIRO, G. M. ; DINIZ, P. R. M. . The Exponentiated Power Generalized Weibull: Properties and Applications. ANAIS DA ACADEMIA BRASILEIRA DE CI\u00caNCIAS (ONLINE), v. 90, p. 2553-2577, 2018. DOI: <a href=\"https:\/\/doi.org\/10.1590\/0001-3765201820170423\">https:\/\/doi.org\/10.1590\/0001-3765201820170423<\/a><\/p>\n<p>2017<\/p>\n<p><strong>BAYER, F. M.<\/strong>; BAYER, D. M.; <strong>PUMI, G.<\/strong>. Kumaraswamy autoregressive moving average models for double bounded environmental data. Journal of Hydrology, v. 555, p. 385-396, 2017.<\/p>\n<p>COUTINHO, VITOR A.; CINTRA, R. J.; <strong>BAYER, F. M.<\/strong>. Low-complexity Multidimensional DCT Approximations for High-order Tensor Data Decorrelation. IEEE Transactions on Image Processing, v. 26, p. 1-1, 2017.<\/p>\n<p><strong>CYBIS, G. B.<\/strong>; SINSHEIMER, J. S. ; BEDFORD, TREVOR ; LEMEY, P. ; SUCHARD, A. M. . Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza. STATISTICS IN MEDICINE, v. 1097, p. sim.7196, 2017.<\/p>\n<p>2016<\/p>\n<p><strong>Horta, Eduardo<\/strong>, and <strong>Flavio Ziegelmann<\/strong>. &#8220;Identifying the spectral representation of Hilbertian time series.&#8221; Statistics &amp; Probability Letters 118 (2016): 45-49.<\/p>\n<p><strong>PRASS, T. S.<\/strong>; LOPES, S\u00cdLVIA R.C. ; ACHCAR, J. A. . MCMC Bayesian Estimation in FIEGARCH Models. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. 45, p. 3238-3258, 2016.<\/p>\n<p>2015<\/p>\n<p><strong>BAYER, F. M.<\/strong>; CRIBARI-NETO, F.. Bootstrap-based model selection criteria for beta regressions. Test, v. 24, p. 776-795, 2015.<\/p>\n<p><strong>CYBIS, GABRIELA B.<\/strong>; SINSHEIMER, JANET S. ; BEDFORD, TREVOR ; MATHER, ALISON E. ; LEMEY, PHILIPPE ; SUCHARD, MARC A. . Assessing phenotypic correlation through the multivariate phylogenetic latent liability model. The Annals of Applied Statistics, v. 9, p. 969-991, 2015.<\/p>\n<p>2014<\/p>\n<p>LOPES, S\u00cdLVIA R.C. ; <strong>PRASS, TAIANE S.<\/strong> . Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes. Physica. A (Print), v. 401, p. 278-307, 2014.<\/p>\n<p>2013<\/p>\n<p><strong>CYBIS, G. B.<\/strong>; SINSHEIMER, J. S. ; LEMEY, P. ; SUCHARD, M. A. . Graph hierarchies for phylogeography. Philosophical Transactions &#8211; Royal Society. Biological Sciences (Print), v. 368, p. 20120206-20120206, 2013.<\/p>\n<p>LOPES, S\u00cdLVIA R.C. ; <strong>PRASS, TAIANE S.<\/strong> . Seasonal FIEGARCH processes. Computational Statistics &amp; Data Analysis (Print), v. 68, p. 262-295, 2013.<\/p>\n<p>2012<\/p>\n<p><strong>Valk, M;<\/strong> Pinheiro AS. 2012. Time-series clustering via quasi U-statistics. Journal of Time Series Analysis. 33:608-619.<\/p>\n<p>Filho, Silva O. C., <strong>Ziegelmann F. A<\/strong>., and Dueker M. (2012). Modeling dependence dynamics through copulas with regime switching, Insurance Mathematics &amp; Economics, Volume 50, p.346-356.<\/p>\n<p>2011<\/p>\n<p>CINTRA, R. J.; <strong>BAYER, F. M.<\/strong>. A DCT Approximation for Image Compression. IEEE Signal Processing Letters, v. 18, p. 579-582, 2011.<\/p>\n<p>2010<\/p>\n<p>Bathia, N., Yao Q., and <strong>Ziegelmann F. A.<\/strong> (2010). Identifying the Finite Dimensionality of Curve Time Series, Annals of Statistics, Volume 38, p.3352-3386.<\/p>\n<p>2002<\/p>\n<p><strong>Ziegelmann, Flavio A.<\/strong> (2002). The Local Exponential Estimator, Econometric Theory, Volume 18, Issue 4, p.985-992, (2002)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>2023 Moresco, Marlon, Marcelo Brutti Righi, and Eduardo Horta. &#8220;Minkowski deviation measures.&#8221; Statistics &amp; Risk Modeling 40.1-2 (2023): 1-19. Bayer, F. M.; Pumi, G.; Pereira, T. L. and Souza, T. C. (2023) Inflated beta autoregressive moving average models. Computational And Applied Mathematics (Forthcoming). Benaduce, H. S. e Pumi, G. (2023). SYMARFIMA: a dynamical model for &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/www.ufrgs.br\/ppgest\/publicacoes\/\"> <span class=\"screen-reader-text\">Publica\u00e7\u00f5es Selecionadas<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/1079"}],"collection":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/comments?post=1079"}],"version-history":[{"count":9,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/1079\/revisions"}],"predecessor-version":[{"id":1200,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/1079\/revisions\/1200"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=1079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}