Anúncios
Research Seminar in Probability and Statistics I - #10
12 janeiro 2015, 16:32 • Manuel Cabral Morais
Anticipative Transmission Planning under Uncertainty
Verena Hagspiel
(Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology)
January 21 (Wedn.), 11:30
Room P3.10
(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)
Transmission system operators (TSOs) build transmission lines to take generation capacity into account. However, their decision is confounded by policies that promote renewable energy technologies. Thus, what should be the size of the transmission line to accommodate subsequent generation expansion? Taking the perspective of a TSO, we use a real options approach not only to determine the optimal timing and sizing of the transmission line but also to explore its effects on generation expansion.
http://math.tecnico.ulisboa.pt/seminars/pe/
Research Seminar in Probability and Statistics I - #8
5 dezembro 2014, 15:18 • Manuel Cabral Morais
Statistical methods in cancer research
Ana Luísa Papoila
(CEAUL, Faculdade de Ciências Médicas da UNL)
December 10 (Wedn.), 11:30
Room P3.10
(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)
Understanding trends and long-term trends in the incidence of diseases, particularly in cancer, is a major concern of epidemiologists. Several statistical methodologies are available to study cancer incidence rates. Age-Period-Cohort (APC) models may be used to study the variation of incidence rates through time. They analyse age-specific incidence according to three time scales: age at diagnosis (age), date of diagnosis (period) and date of birth (cohort). Classic and Bayesian APC models are available. Understanding geographical variations in health, particularly in small areas, has also become extremely important. Several types of spatial epidemiology studies are available such as disease mapping, usually used in ecological studies. The geographic mapping of diseases is very important in the definition of policies in oncology, namely on the allocation of resources, and on the identification of clusters with high incidence of disease. Geographical association studies, that allow the identification of risk factors associated with the spatial variation of a disease, are also indispensable and deserve special attention in disease incidence studies. For this purpose, Bayesian Hierarchical models are a common choice.
To quantify cancer survival in the absence of other causes of death, relative survival is also considered in cancer population-based studies. Several approaches to estimate regression models for relative survival using the method of maximum likelihood are available.
Finally, having an idea of the future burden of cancer is also of the utmost importance, namely for planning health services. This is why projections of cancer incidence are so important. Several projection models that differ according to cancer incidence trends are available.
The aim of this study is to investigate spatial and temporal trends in the incidence of colorectal cancer, to estimate relative survival and to make projections. It is a retrospective population-based study that considers data on all colorectal cancers registered by the Southern Portuguese Cancer Registry (ROR Sul) between 1998 and 2006.
Research Seminar in Probability and Statistics I - #7
21 novembro 2014, 13:56 • Manuel Cabral Morais
Testes de hipóteses para comparar probabilidades de recombinação de dois seres vivos com um conjunto de marcadores comuns
Ana Freitas
(Instituto Superior de Educação e Ciências; CEMAT)
December 3 (Wedn.), 11:30
Room P3.10
(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)
A recombinação genética, embora seja um fenómeno estudado desde há longo tempo, continua a ser um tema muito actual, pois encontra-se bastante relacionada com a evolução e diversificação das espécies. É um processo que gera novas combinações de genes onde depois a selecção natural actua. Por outro lado, é com base nas estimativas das probabilidades de recombinação que são construídos os mapas genéticos que nos dão uma imagem da constituição dos diversos cromossomas que existem em cada espécie. O objectivo deste trabalho é apresentar um método, se possível exacto, para estimar a estrutura de variância-covariância dos estimadores conjuntos das probabilidades de recombinação genética, e propor testes de hipóteses para comparar probabilidades de recombinação entre dois grupos de seres vivos que tenham um conjunto de marcadores comuns. As metodologias propostas são aplicadas a um conjunto de dados relativos a uma espécie de Eucaliptos (Eucalyptus globulus) resultantes de um cruzamento obtido por backcross. Os resultados obtidos, permitem-nos concluir que as metodologias propostas são apropriadas para comparar probabilidades de recombinações entre os dois sexos de uma espécie e, podem constituir um método alternativo aos testes habitualmente utilizados para resolver este tipo de questões.
Research Seminar in Probability and Statistics I - #9
12 novembro 2014, 14:33 • Manuel Cabral Morais
A robust mixed linear model for heritability estimation in plant studies
Vanda M. Lourenço
(FCT, Universidade Nova de Lisboa; CEMAT)
December 17 (Wedn.), 11:30
Room P3.10
(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)
Heritability (H2) refers to the extent of how much a certain phenotype is genetically determined. Knowledge of H2 is crucial in plant studies to help perform effective selection. Once a trait is known to be high heritable, association studies are performed so that the SNPs underlying those traits’ variation may be found. Here, regression models are used to test for associations between phenotype and candidate SNPs. SNP imputation ensures that marker information is complete, so both the coefficient of determination (R2) and H2 are equivalent. One popular model used in these studies is the animal model, which is a linear mixed model (LMM) with a specific layout. However, when the normality assumption is violated, as other likelihood-based models, this model may provide biased results in the association analysis and greatly affect the classical R2. Therefore, a robust version of the REML estimates for linear LMM to be used in this context is proposed, as well as a robust version of a recently proposed R2. The performance of both classical and robust approaches for the estimation of H2 is thus evaluated via simulation and an example of application with a maize data set is presented.
Joint work with P.C. Rodrigues, M.S. Fonseca and A.M. Pires
Research Seminar in Probability and Statistics I - #6
7 novembro 2014, 11:30 • Manuel Cabral Morais
Network Inference from Co-Occurrences
Mário A. T. Figueiredo
(Instituto de Telecomunicações, IST)
November 19 (Wedn.), 11:30
Room P3.10
(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)
Inferring network structures is a central problem arising in many fields of science and technology, including communication systems, biology, sociology, and neuroscience. In this talk, after briefly reviewing several network inference problems, we will focus on that of inferring network structure from ``co-occurrence" observations. These observations identify which network components (e.g., switches, routers, genes) co-occur in a path, but do not indicate the order in which they occur in that path. Without order information, the number of structures that are data-consistent grows exponentially with the network size. Yet, the basic engineering/evolutionary principles underlying most networks strongly suggest that no all data-consistent structures are equally likely. In particular, nodes that often co-occur are probably closer than nodes that rarely co-occur. This observation suggests modeling co-occurrence observations as independent realizations of a random walk on the network, subjected to random permutations. Treating these permutations as missing data, allows deriving an expectation–maximization (EM) algorithm for estimating the random walk parameters. The model and EM algorithm significantly simplify the problem, but the computational complexity still grows exponentially in the length of each path. We thus propose a polynomial-time Monte Carlo EM algorithm based on importance sampling and derive conditions that ensure convergence of the algorithm with high probability. Finally, we report simulations and experiments with Internet measurements and inference of biological networks that demonstrate the performance of this approach.
The work reported in this talk was done in collaboration with Michael Rabbat (McGill University, Canada) and Robert D. Nowak (University of Wisconsin, USA).
http://math.tecnico.ulisboa.pt/seminars/pe/