{"id":1087,"date":"2024-09-30T15:22:16","date_gmt":"2024-09-30T15:22:16","guid":{"rendered":"http:\/\/localhost:8000\/?p=1087"},"modified":"2026-08-03T16:37:11","modified_gmt":"2026-08-03T16:37:11","slug":"i-encontro-de-egressos-do-ppge","status":"publish","type":"post","link":"https:\/\/ppge.im.ufrj.br\/en\/i-encontro-de-egressos-do-ppge\/","title":{"rendered":"1st Meeting of PPGE Alumni"},"content":{"rendered":"

Hor\u00e1rio<\/strong><\/h3>\n\n\n\n
Dia 10 – Quinta-feira<\/strong><\/p>\n

10:00h Abertura –\u00a0Slides<\/a><\/p>\n

10:20h Eduardo Ferioli Gomes – Slides<\/a><\/p>\n

11:00h Patr\u00edcia Lusi\u00e9 Velozo da Costa – Slides<\/a><\/p>\n

Intervalo para almo\u00e7o 11:40h-13:20h<\/p>\n

13:20h Vinicius Pinheiro Israel –\u00a0Slides<\/a><\/p>\n

Intervalo – 14:00h-14:10h<\/p>\n

14:10h Rafael Erbisti – Slides<\/a><\/p>\n

14:50h Guilherme dos Santos – Slides<\/a><\/p>\n

caf\u00e9 e conversa \u2013 15:30h-16:10h<\/p>\n

16:10 Josiane S. Cordeiro – Slides<\/a><\/p>\n

Jantar de confraterniza\u00e7\u00e3o – 19:00<\/td>\n

Dia 11 – Sexta-feira<\/strong><\/p>\n

10:20h Vitor Capdeville –\u00a0Slides<\/a><\/p>\n

11:00h Rafael Santos – Slides<\/a><\/p>\n

Intervalo para almo\u00e7o 11:40-13:20h<\/p>\n

13:20h Jony Arrais Pinto Junior –\u00a0Slides<\/a><\/p>\n

Intervalo – 14:00h-14:10h<\/p>\n

14:10h Pamela\u00a0 M. Chiroque-Solano (online) – Slides<\/a><\/p>\n

14:50h Guido Alberti Moreira (online) – Slides<\/a><\/p>\n

caf\u00e9 e conversa: 15:30-16:10<\/p>\n

16:10h Marcus L. Nascimento – Slides<\/a><\/p>\n

16:50h Encerramento<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Resumos das palestras<\/strong><\/h3>\n

Palestrante: Eduardo Ferioli Gomes (UFF)<\/strong>
\nT\u00edtulo: Directional High Frequency Trading in the Kyle-Back Model<\/strong><\/h4>\n

Resumo: In traditional Kyle-Back models, the only source of information comes from the insider\u2019s signal. We consider a more realistic version of the Kyle-Back model with a private and a public signal. The insider observes both signals. The private signal, that is only directly observed by the insider, may be static, when the insider knows the value of the asset in advance, or dynamic, when it converges to the true value of the asset at the end of the trading period. The market maker receives a dynamic signal that also converges to the true value of the asset at the end of the trading period.<\/p>\n

In the dynamic case, we prove that the insider\u2019s valuation of the asset is given by a linear combination of both the public and private signals and it is a martingale for the insider\u2019s filtration.<\/p>\n

Furthermore, we show that the price – which is the market maker\u2019s valuation of the asset – is also given by a linear combination of the public signal and the weighted demand. In addition, it is proven that it converges to the true price of the asset as it is expected in the traditional theory.<\/p>\n

An interesting fact that is observed is that it is possible to see an increase in the volatility of the price in the end of the trading period when trading becomes aggressive due to the convergence of both signals to the true price of the asset.<\/p>\n

Palestrante: Guido Alberti Moreira (Universidade do Minho, Portugal)<\/strong>
\nT\u00edtulo: Presence-Only for Marked Point Process Under Preferential Sampling<\/strong><\/h4>\n

Resumo: Preferential sampling models have garnered significant attention in recent years. Although the original model was developed for geostatistics, it found applications in other types of data, such as point processes in the form of presence-only data. While this has been recognized in the Statistics literature, there is value in incorporating ideas from both presence-only and preferential sampling literature. In this paper, we propose a novel model that extends existing ideas to handle a continuous variable collected through opportunistic sampling. To demonstrate the potential of our approach, we apply it to sardine biomass data collected during commercial fishing trips. While the data is intuitively understood, it poses challenges due to two types of preferential sampling: fishing events (presence data) are non-random samples of the region, and fishermen tend to set their nets in areas with a high quality and value of catch (i.e., bigger schools of the target species). We discuss theoretical and practical aspects of the problem, and propose a well-defined probabilistic approach. Our approach employs a data augmentation scheme that predicts the number of unobserved fishing locations and corresponding biomass (in kg). This allows for evaluation of the Poisson Process likelihood without the need for numerical approximations. The results of our case study may serve as an incentive to use data collected during commercial fishing trips for decision-making aimed at benefiting both ecological and economic aspects. The proposed methodology has potential applications in a variety of fields, including ecology and epidemiology, where marked point process model are commonly used.<\/p>\n

Palestrante: Guilherme dos Santos\u00a0 (UFRJ)<\/strong>
\nT\u00edtulo: A multivariate approach for correcting reporting delays in infectious disease surveillance<\/strong><\/h4>\n

Resumo: Frequently, real-time tracking of epidemics is faced with a concerning issue, the reporting delays of cases and deaths. Delays might occur due to logistical problems, laboratory confirmation, and other reasons. Being able to correct the delay is essential to decision-making with the goal of containing an epidemic. In some cases, the epidemic might be associated with more than one disease, Dengue and Chikungunya are common examples of this phenomenon. We propose a multivariate model to correct reporting delays and accommodate the above-mentioned cases. The model is estimated using the Integrated Nested Laplace Approximation method with the aim of providing faster results. An application for the corrections of reporting delays of Dengue and Chikungunya in the state of Rio de Janeiro during an epidemic in 2019 is provided.<\/p>\n

Keywords: Nowcasting; Dengue; Chikungunya; INLA; Bayesian hierarchical model.<\/p>\n

Palestrante: Jony Arrais Pinto Junior\u00a0 (UFF)
\nT\u00edtulo: A Jornada de uma D\u00e9cada ap\u00f3s o Doutorado na UFRJ<\/strong><\/h4>\n

Resumo: O objetivo desta apresenta\u00e7\u00e3o \u00e9 compartilhar minhas experi\u00eancias profissionais desde a conclus\u00e3o do doutorado em 2014, com \u00eanfase no meu papel como professor e pesquisador no Departamento de Estat\u00edstica da Universidade Federal Fluminense (UFF).<\/p>\n

Ao longo dessa d\u00e9cada, colaborei com diversos centros de pesquisa, como o IPEA, FIOCRUZ e UFBA, fortalecendo minhas contribui\u00e7\u00f5es nas mais diversas \u00e1reas. Um ponto de grande relev\u00e2ncia tem sido a forma\u00e7\u00e3o de novos talentos da gradua\u00e7\u00e3o em Estat\u00edstica na UFF, muitos dos quais est\u00e3o sendo absorvidos por renomados programas de p\u00f3s-gradua\u00e7\u00e3o, como o da UFRJ. Tamb\u00e9m abordarei as dire\u00e7\u00f5es atuais da minha pesquisa, que incluem an\u00e1lise de classes latentes espaciais e o estudo de dados longitudinais, destacando as possibilidades e avan\u00e7os na \u00e1rea.<\/p>\n

Palestrante: Josiane S Cordeiro (UFRRJ)<\/strong>
\nT\u00edtulo: Energy Consumption Forecast in the Brazilian Industrial Sector: Integration of Bottom-Up and Top-Down Methods with Monte Carlo Simulation<\/strong><\/h4>\n

Resumo: The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sector and organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that combines bottom-up and top-down methods using Monte Carlo simulation for electricity consumption forecasting. The exponential smoothing and Box-Jenkins models were used to generate the projections of the individual series. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.<\/p>\n

Palestrante: Marcus L. Nascimento (FGV-EMAp e Funda\u00e7\u00e3o Jos\u00e9 Luis Egydio Set\u00fabal)<\/strong>
\nT\u00edtulo: An Expectation-Maximization algorithm for noncrossing Bayesian quantile regression.<\/strong><\/h4>\n

Resumo: When quantiles are fitted separately, the resultant regression lines may cross, violating the basic probabilistic rule that quantiles are monotonic functions and possibly causing problems for inference in practice. Using location-scale mixture representation of asymmetric Laplace distribution (ALD), we write a joint posterior density function for all quantile levels of interest and develop a constrained Expectation-Maximization algorithm that handles crossing issues.<\/p>\n

Palestrante: Pamela M. Chiroque-Solano (University of Regensburg)<\/strong>
\nT\u00edtulo: Probabilistic Models and Machine Learning Algorithms for Biomass and LAI Prediction Using Multispectral and LiDAR UAV Data.<\/strong><\/h4>\n

Resumo: Recent advances in remote sensing have revolutionized environmental monitoring by enabling the integration of multispectral and LiDAR data from UAV platforms. This dual-capability technology provides high-resolution spectral and 3D geometric information critical for precision agriculture and forestry applications. Specifically, the prediction of biomass and leaf area index (LAI)\u2014key indicators for ecosystem health, carbon sequestration, and resource management\u2014has become more accurate and accessible. However, the inherent heterogeneity and multicollinearity in the derived features present significant challenges in model development, requiring sophisticated approaches for optimal predictive performance. This study explores the comparative efficacy of probabilistic models and machine learning algorithms for predicting biomass and LAI from UAV-based multispectral and LiDAR data. By incorporating advanced variable selection methods, our approach mitigates issues of overfitting, reduces bias, and utilizes algorithms capable of handling non-linear outcomes, leading to more reliable predictions in both agricultural and forestry settings. More than thirty models were applied to two distinct datasets.<\/p>\n

Our results underscore the critical importance of methodical model selection criteria, where probabilistic approaches and machine learning algorithms can be compared, highlighting their advantages and disadvantages, even under different assumptions. Each approach offers distinct benefits depending on the data characteristics and the specific application, making post-hoc metrics that reconcile these paradigms essential.<\/p>\n

Although many methods are compared in this work, others can be considered as well. The outcomes can be used as insights for strategies for biomass and LAI prediction, offering a clear path toward more accurate, scalable solutions in environmental monitoring. Our results highlight not only the practical utility of UAV-derived multispectral and LiDAR data but also the role of rigorous model comparison in advancing remote sensing applications in agriculture and forestry.<\/p>\n

Trabalho em conjunto com: Lu\u00eds Padua e Domingos Manuel Mendes Lopes (University of Tr\u00e1s-os-Montes e Alto Douro).<\/p>\n

Palestrante: Patr\u00edcia Lusi\u00e9 Velozo da Costa (UFF)<\/strong>
\nT\u00edtulo: Entre Modelos e Maternidade: Desafios e Experi\u00eancias Vivenciadas ap\u00f3s o Doutorado.<\/strong><\/p>\n

Resumo: Nesta apresenta\u00e7\u00e3o, compartilharei minha trajet\u00f3ria desde a conclus\u00e3o do doutorado, abordando as experi\u00eancias que marcaram minha vida acad\u00eamica ao longo da \u00faltima d\u00e9cada. Refletirei sobre meu papel como professora e pesquisadora no Departamento de Estat\u00edstica da Universidade Federal Fluminense, onde assumi responsabilidades como Chefia de Departamento e a organiza\u00e7\u00e3o de eventos como a Semana da Estat\u00edstica e o Hackathon. Al\u00e9m disso, abordarei as dire\u00e7\u00f5es atuais da minha pesquisa, com foco em dois projetos principais: an\u00e1lise de dados inflacionados de zero e mortalidade materna. Ambos os estudos aplicam modelos lineares generalizados, com a estima\u00e7\u00e3o de par\u00e2metros realizada sob a abordagem Bayesiana, utilizando o m\u00e9todo de Monte Carlo Hamiltoniano. Em particular, os dados inflacionados de zero s\u00e3o ajustados por meio de modelos de mistura, como o modelo Poisson inflacionado de zero.<\/p>\n

Palestrante: Rafael Erbisti (UFF)<\/strong>
\nT\u00edtulo: ARBOALVO: Bayesian spatiotemporal learning and predictive model for dengue<\/strong><\/h4>\n

Resumo: Transmission of urban arboviruses does not occur homogeneously across territories and varies over time. Estimating the risk of dengue through statistical models that consider simultaneous variability in space and time provides more realistic estimates of transmission dynamics, facilitating the identification of priority areas. Additionally, such models enable predictions for timely actions in controlling and surveying urban arboviruses, such as dengue, chikungunya, and Zika. We analyzed the reported cases of dengue by epidemiological week and neighborhood in Natal-RN between 2015 and 2018. Temporal Conditional Autoregressive models are fitted. The predictor comprised a set of entomological, climatic, and sociosanitary indicators with temporal lags, an offset term, and structures of temporal and spatial dependence. Fitting was performed using the Integrated Nested Laplace Approximation method. We forecast dengue case counts for the next four weeks, accounting for both non-occurrences and fluctuations in the time series during periods of non-zero occurrences. Predictive maps of weekly risk dynamics were obtained, allowing timely identification of neighborhoods with high and persistent dengue risk. The best model indicated a significant increase in the probability of dengue occurrence in the observation week, with an increase of one standard deviation in reported cases in the previous week, Aedes egg positivity index from the previous four weeks, and mean daytime temperature in the preceding 6\u20138 weeks. There was an increase in dengue risk with an increase of one standard deviation in the density of the poor population per occupied area and in the mean Aedes egg density index in the previous 3\u20135 weeks. The proposed Bayesian space-time analysis can contribute to the operational control of dengue and Aedes Aegypti by detecting priority areas and predicting dengue cases for the next four weeks. Additionally, it identified and quantified the influences of entomological, sociosanitary, climatic, and demographic indicators.<\/p>\n

Palestrante: Rafael Souza dos Santos (UFRJ)<\/strong>
\nT\u00edtulo: Processo de contato sob renova\u00e7\u00f5es<\/strong><\/h4>\n

Resumo: Na apresenta\u00e7\u00e3o eu vou falar sobre o processo de contato sob renova\u00e7\u00f5es, um assunto no qual trabalhei durante meu p\u00f3s-doutorado e sigo trabalhando como professor adjunto do Departamento de M\u00e9todos Estat\u00edsticos da UFRJ. O processo de contato (cl\u00e1ssico) busca modelar a evolu\u00e7\u00e3o de uma doen\u00e7a infecciosa do seguinte modo: Indiv\u00edduos infectados transmitem a doen\u00e7a para seus vizinhos ap\u00f3s tempos exponenciais de taxa \u03bb > 0 e ficam curados ap\u00f3s tempos exponenciais de taxa 1. J\u00e1 o processo de contato sob renova\u00e7\u00f5es flexibiliza esse modelo cl\u00e1ssico, permitindo que os tempos at\u00e9 a cura da doen\u00e7a tenham distribui\u00e7\u00f5es mais gerais. A ideia \u00e9 que a apresenta\u00e7\u00e3o seja a mais inclusiva poss\u00edvel, de modo que n\u00e3o vou entrar em detalhes t\u00e9cnicos sobre o modelo.<\/p>\n

Palestrante:\u00a0 Vinicius Pinheiro Israel (UNIRIO)
\nT\u00edtulo:\u00a0 Puni\u00e7\u00e3o e liberdades: encarceramento em massa, seletividade penal e popula\u00e7\u00e3o escondida<\/h4>\n

Resumo: O encarceramento em massa \u00e9 um problema central das democracias modernas e impacta diretamente nas chances de vida de grande parcela da popula\u00e7\u00e3o. No Brasil, o n\u00famero de pessoas presas teve um aumento de cerca de 600% desde 1990, o pa\u00eds possui mais de 750 mil presos, colocando-se na terceira posi\u00e7\u00e3o entre os que mais encarceram no mundo. As explica\u00e7\u00f5es sobre o aumento carcer\u00e1rio norteiam-se pela ideia de reorienta\u00e7\u00e3o liberal, pelo fortalecimento de ideias conservadoras de combate ao crime e pelo aumento da demanda por mais puni\u00e7\u00e3o. A espinha dorsal dos argumentos no Brasil enfatiza a passagem do regime autorit\u00e1rio ditatorial para a democracia, marcada pela resist\u00eancia de policiais e funcion\u00e1rios do sistema penitenci\u00e1rio ao novo regime e por uma acentuada instabilidade pol\u00edtica na esfera da seguran\u00e7a p\u00fablica. A partir de um di\u00e1logo com a estratifica\u00e7\u00e3o social, teoria pol\u00edtica e metodologia quantitativa, o presente livro busca compreender as causas e consequ\u00eancias desse fen\u00f4meno.<\/p>\n

Palestrante: Vitor Capdeville (ESSOR Seguros)
\nT\u00edtulo: I<\/strong>ntegra\u00e7\u00e3o de C\u00e1lculos Atuariais e Solu\u00e7\u00f5es de ETL com Python em Ambientes Corporativos<\/h4>\n

Resumo: Durante minha atua\u00e7\u00e3o na MAG Seguros e Essor, enfrentei desafios relacionados \u00e0 centraliza\u00e7\u00e3o de c\u00e1lculos atuariais e \u00e0 automa\u00e7\u00e3o de processos de manipula\u00e7\u00e3o de dados. Desenvolvi solu\u00e7\u00f5es utilizando Python que otimizam tanto a execu\u00e7\u00e3o de c\u00e1lculos complexos quanto a gest\u00e3o de pipelines de dados. O primeiro projeto, um pacote de c\u00e1lculos atuariais, permitiu que atu\u00e1rios tivessem mais controle sobre a l\u00f3gica de c\u00e1lculo de pr\u00eamios e reservas, integrando uma API em FastAPI com o sistema da companhia. J\u00e1 o pacote ExTraLo foi criado para simplificar processos de ETL, oferecendo uma interface flex\u00edvel que extrai, valida, transforma e carrega dados de fontes diversas.<\/p>\n

Al\u00e9m disso, apresento o Psitest, um projeto em desenvolvimento voltado \u00e0 aplica\u00e7\u00e3o de testes psicol\u00f3gicos em ambiente digital. A solu\u00e7\u00e3o envolve a corre\u00e7\u00e3o automatizada de testes a partir de imagens e utiliza t\u00e9cnicas de vis\u00e3o computacional para identificar e classificar respostas.<\/p>","protected":false},"excerpt":{"rendered":"

Hor\u00e1rio Dia 10 – Quinta-feira 10:00h Abertura –\u00a0Slides 10:20h Eduardo […]<\/p>\n","protected":false},"author":1,"featured_media":1090,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8,4],"tags":[],"class_list":["post-1087","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-congressos-e-simposios","category-noticias-gerais"],"_links":{"self":[{"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/posts\/1087","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/comments?post=1087"}],"version-history":[{"count":9,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/posts\/1087\/revisions"}],"predecessor-version":[{"id":1268,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/posts\/1087\/revisions\/1268"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/media\/1090"}],"wp:attachment":[{"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/media?parent=1087"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/categories?post=1087"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ppge.im.ufrj.br\/en\/wp-json\/wp\/v2\/tags?post=1087"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}