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16/12/2009, 10:00 — 11:00 — Room P3.10, Mathematics Building
Gonçalo dos Reis, CMAP- École Polytechnique (Paris)

Backward stochastic differential equations and quasi-liner PDEs

In the spirit of a forthcoming research project within CEMAT this talk aims at introducing a probabilistic approach to PDE. This probabilistic interpretation for systems of second order quasilinear parabolic PDE is obtained by establishing a kind of backward stochastic differential equation. We look at several aspects of this link.

11/12/2009, 16:00 — 17:00 — Room P12, Mathematics Building
Maria da Graça Magalhães, Edviges Coelho, Instituto Nacional de Estatística

Methods and techniques to construct projections of resident population: Portugal, 2008-2060

The purpose of this communication is to present the methodology adopted in the last exercise of resident population projections in Portugal, carried out by the Statistics Portugal.

These population projections are based on the concept of resident population and adopt the cohort-component method, where the initial population is grouped into cohorts defined by age and sex, and continuously updated, according to the assumptions of future development set for each of the components of population change - fertility, mortality and migration - that is, by adding the natural balance and net migration, in addition to the natural aging process. This method, widely used in the elaboration of population projections at national level, allows the development of different scenarios of demographic evolution based on different combinations of likely developments of the components.

The results are conditioned, on the one hand by the structure and composition of the initial population, and on the other, by the different behaviour patterns of fertility, mortality and migration in each set of assumptions about the evolution over the projection period, so it should be emphasize the conditional nature of the results, since it is a method of scenarios of "if ... then ..." in that each combines differently the assumptions outlined for the components.

Given the importance of the projections of individual components to the outcome of the exercise, we proceed to the presentation of the methodologies used in the projection of each of these. The projection of components is carried out using a set of statistical methods, adequate to the background information and the proposed target. Thus in the case of fertility we have modelled the fertility rates using the method proposed by Schmertmann (2003), for mortality we have used the Poisson-Lee-Carter with limit life table proposed by Bravo (2007) and for migration, given the increased fragility of the data and consequently the difficulties regarding the practical application of methods for statistical modelling, was adopted as a initial reference the average of the estimated flows in the last 15 years. Finally, we will present the main results of this exercise, both in regard to components and to the future population.

27/11/2009, 16:00 — 17:00 — Room P12, Mathematics Building
Hannes Helgason, Ecole Normale Superieure - Lyon

Nonparametric estimation of highly oscillatory signals

We will consider the problem of estimating highly oscillatory signals from noisy measurements. These signals are often referred to as chirps in the literature; they are found everywhere in nature, and frequently arise in scientific and engineering problems. Mathematically, they can be written in the general form A(t) exp(ilambda varphi(t)), where lambda is a large constant base frequency, the phase varphi(t) is time-varying, and the envelope A(t) is slowly varying. Given a sequence of noisy measurements, we study the problem of estimating this chirp from the data.

We introduce novel, flexible and practical strategies for addressing these important nonparametric statistical problems. The main idea is to calculate correlations of the data with a rich family of local templates in a first step, the multiscale chirplets, and in a second step, search for meaningful aggregations or chains of chirplets which provide a good global fit to the data. From a physical viewpoint, these chains correspond to realistic signals since they model arbitrary chirps. From an algorithmic viewpoint, these chains are identified as paths in a convenient graph. The key point is that this important underlying graph structure allows to unleash very effective algorithms such as network flow algorithms for finding those chains which optimize a near optimal trade-off between goodness of fit and complexity.

Our estimation procedures provide provably near optimal performance over a wide range of chirps and numerical experiments show that our estimation procedures perform exceptionally well over a broad class of chirps.

20/11/2009, 16:00 — 17:00 — Room P12, Mathematics Building
Rui Paulo, ISEG and CEMAPRE, Technical University of Lisbon

Validation of Computer Models with Multivariate Output

We consider the problem of validating computer models that produce multivariate output, particularly when the model is computationally demanding. Our strategy builds on Gaussian process-based response-surface approximations to the output of the computer model independently constructed for each of its components. These are then combined in a statistical model involving field observations to produce a predictor of the multivariate output at untested input vectors. We illustrate the methodology in a situation where the output consists of a two-dimensional output of very irregular functions.

30/10/2009, 16:00 — 17:00 — Room P12, Mathematics Building
Maria Kulikova, Universidade Técnica de Lisboa - Instituto Superior Técnico e CEMAT

Estimation of stochastic volatility models through adaptive Kalman filtering methods

Volatility is a central concept when dealing with financial applications. It is usually equated with the risk and plays a central role in the pricing of derivative securities. It is also widely acknowledged nowadays that volatility is both time-varying and predictable, and stochastic volatility models are commonplace. The approach based on autoregressive conditional heteroscedasticity (ARCH) introduced by Engle, and later generalized to GARCH by Bollerslev, was the first attempt to take into account the changes in volatility over time. The class of stochastic volatility (SV) models is now recognized as a powerful alternative to the traditional and widely used ARCH/GARCH approach. We focus on the maximum likelihood estimation of the class of stochastic volatility models. The main technique is based on the Kalman filter (KF), which is known to be numerically unstable. Using the advanced array square-root form of the KF, we construct a new square-root algorithm for the log-likelihood gradient (score) evaluation. This avoids the use of the conventional KF with its inherent numerical instabilities and improves the robustness of computations against roundoff errors. The proposed square-root adaptive KF scheme is ideal for simultaneous parameter estimation and extraction of the latent volatility series.

21/10/2009, 10:00 — 11:00 — Room P3.10, Mathematics Building
Graciela Boente, Universidad de Buenos Aires and CONICET, Argentina

Robust estimators in functional principal components

When dealing with multivariate data, like classical PCA, robust PCA searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator s_n may be used in the maximization problem. This approach was first in Li and Chen (1985) while a maximization algorithm was proposed in Croux and Ruiz-Gazen (1996) and their influence function was derived by Croux and Ruiz-Gazen (2005). Recently, their asymptotic distribution was studied in Cui et al. (2003).

Let X(t) be a stochastic process with continuous trajectories and finite second moment, defined on a finite interval. We will denote by Γ(t,s)=cov(X(t),X(s)) its covariance function and by ϕ j and λ j the eigenfunctions and the eigenvalues of the covariance operator with λ j in the decreasing order. Dauxois et al. (1982) derived the asymptotic properties of non-smooth principal components of functional data obtained by considering the eigenfunctions of the sample covariance operator. On the other hand, Silverman (1996) and Ramsay and Silverman (1997), introduced smooth principal components for functional data, based on roughness penalty methods while Boente and Fraiman (2000) considered a kernel-based approach. More recent work, dealing with estimation of the principal components of the covariance function, includes Gervini (2006), Hall and Hosseini-Nasab (2006), Hall et al. (2006) and Yao and Lee (2006). Up to our knowledge, the first attempt to provide estimators of the principal components less sensitive to anomalous observations was done by Locantore et al. (1999) who considered the coefficients of a basis expansion. Besides, Gervini (2008) studied a fully functional approach to robust estimation of the principal components by considering a functional version of the spherical principal components defined in Locantore et al. (1999). On the other hand, Hyndman and Ullah (2007) provide a method combining a robust projection-pursuit approach and a smoothing and weighting step to forecast age-specific mortality and fertility rates observed over time.

In this talk, we introduce robust estimators of the principal components and we obtain their consistency under mild conditions. Our approach combines robust projection-pursuit with different smoothing methods.

09/10/2009, 16:00 — 17:00 — Room P1, Mathematics Building
Maria do Rosário Oliveira, Departmento de Matemática - Instituto Superior Técnico e CEMAT

Testes de diagnóstico versus métodos de detecção de anomalias: a estatística a ultrapassar barreiras

Na literatura médica, os problemas inerentes à avaliação do desempenho de testes de diagnóstico têm sido largamente estudados. Os méritos e limitações das várias abordagens são conhecidos e discutidos em variados cenários e contextos. O conhecimento adquirido nesta área pode ser usado para avaliar o desempenho de métodos detecção de anomalias na ausência de um ground truth. Em Telecomunicações, as anomalias na transmissão de dados são identificadas por eventos inesperados e desajustados ao normal fluxo dos mesmos. Na prática, podem traduzir-se em invasões a computadores alheios ou outros transtornos de grande impacto nas nossas vidas.

Nesta comunicação estabelece-se o paralelismo entre os indicadores frequentemente usados, pela comunidade médica, na avaliação do desempenho de técnicas laboratoriais e os indicadores para aferir a qualidade de um método de detecção de anomalias, pelos profissionais de Engenharia. A utilização de um ground truth imperfeito ou parcial, como referência na avaliação dos métodos de detecção de anomalias, é questionada ilustrando-se o enviesamento obtido. Por fim, o modelo de classes latentes é apontado como a solução adequada para a comparação do desempenho de métodos de detecção de anomalias na ausência do ground truth, tal como é utilizado na avaliação do desempenho de técnicas de diagnóstico na ausência de um gold standard.

28/09/2009, 10:00 — 11:00 — Room P3.10, Mathematics Building
Elena Almaraz Luengo, Universidad Complutense de Madrid, Spain

Some Applications of Stochastic Dominance in Economy

There exists a vast range of applications of Stochastic Dominance (SD) rules in different areas of knowledge, such as: Mathematics, Statistic, Biology, Sociology, Economy, etc. Currently, the main areas of application of SD in Economics and Finance are: efficient portfolio selection, asset valuation, risk, insurance, etc. In this talk we will show the utility of SD in Economics.For that, we will start by explaining the classic concepts of SD and their economic interpretation, as well as other definitions used in this context (likelihood ratio order, hazard rate order, Lorenz’s order level crossing order, etc). One of the main topics we will treat is optimal portfolio selection and its relation with associated weighted random variables and utility functions. In particular, we will establish relations between the utilities of the weighted random variables, given the stochastic relations of the original random variables from which we obtained the weighted random variables. Another context in which SD rules are applied is the ruin and risk problems; we will show a generalization of the classic ruin mode and some SD relations between ruin times of two (stochastic) risk processes. Also SD rules can be used in asset valuation context; we will treat, as an example, the Cox and Rubinstein’s model. Others applications of SD rules will also be commented, including: Black Scholes’ model, integral stochastic calculus, inventory theory, chains, etc.

28/07/2009, 11:00 — 12:00 — Room P3.10, Mathematics Building
Graciela Boente, Universidad de Buenos Aires and CONICET

Robust methods in semiparametric estimation with missing responses

Most of the statistical methods in nonparametric regression are designed for complete data sets and problems arise when missing observations are present which is a common situation in biomedical or socioeconomic studies, for example. Classic examples are found in the field of social sciences with the problem of non-response in sample surveys, in Physics, in Genetics (Meng, 2000), among others. We will consider inference with an incomplete data set where the responses satisfy a semiparametric partly linear regression model. We will introduce a family of robust procedures to estimate the regression parameter as well as the marginal location of the responses, when there are missing observations in the response variable, but the covariates are totally observed. In this context, it is necessary to require some conditions regarding the loss of an observation. We model the aforementioned loss assuming that the data are missing at random, i.e, the probability of observing a missing data is independent of the response variable, and it only depends on the covariate. Our proposal is based on a robust profile likelihood approach adapted to the presence of missing data. The asymptotic behavior of the robust estimators for the regression parameter is derived. Several proposals for the marginal location are considered. A Monte Carlo study is carried out to compare the performance of the robust proposed estimators among them and also with the classical ones, in normal and contaminated samples, under different missing data models.

21/07/2009, 14:30 — 15:30 — Room P3, Mathematics Building, IST
Wolfgang Schmid, Department of Statistics, European University Viadrina, Frankfurt, Germany

Local Approaches for Simultaneous Interpolating of Air Pollution Processes

In the paper, we derive a non-linear cokriging predictor for spatial interpolating of multivariate environmental process. The suggested predictor is based on the locally weighted scatterplot smoothing method of Cleveland (1979) applied simultaneously to several processes. This approach is more flexible as the linear cokriging predictor usually applied in mulivariate environmental statistics and extends the LOESS predictor of Bodnar and Schmid (2009) to multivariate data. In an empirical study, we apply the suggested approach for interpolating the most significant air pollutants in the Berlin/Brandenburg region.

26/05/2009, 16:30 — 17:30 — Amphitheatre Pa2, Mathematics Building
Carlos Soares, Faculdade de Economia, Universidade do Porto

Datasetoids: generating more data for empirical data analysis studies

With the increase in the number of models induced from data that are used by organizations for decision support, the problem of algorithm (and parameter) selection is becoming increasingly important. Two approaches to obtain empirical knowledge that is useful for that purpose are empirical studies and metalearning. However, most empirical (meta)knowledge is obtained from a relatively small set of datasets. In this paper, we propose a method to obtain a large number of datasets which is based on a simple transformation of existing datasets, referred to as datasetoids. We test our approach on the problem of using metalearning to predict when to prune decision trees. The results show significant improvement when using datasetoids. Additionally, we identify a number of potential anomalies in the generated datasetoids and propose methods to solve them.

24/03/2009, 15:00 — 16:00 — Room P3.10, Mathematics Building
Mohan Chaudhry, Royal Military College of Canada

Numerical Inversion of Transforms Occurring in Queueing and Other Stochastic Processes

We consider the numerical inversion of three classes of generating functions (GFs): classes of probability generating functions (PGFs) that are given in rational and non-rational forms, and a class of GFs that are not PGFs. Particular emphasis is on those PGFs that are not explicitly given but contain a number of unknowns. We show that the desired sequence can be obtained to any given accuracy, so long as enough numerical precision is used.
A Sala vai ser a P1 - ATENÇÂO

18/02/2009, 16:00 — 17:00 — Room P12, Mathematics Building
Patrícia Ferreira, CEMAT and Instituto Superior Técnico

Performance analysis of joint control schemes for the process mean (vector) and (co)variance (matrix)

The presentation focus on the ongoing and future work on the performance analysis of joint control schemes for the process mean (vector) and (co)variance (matrix), when the usual assumptions of independence and normality are no longer valid. We shall give special attention to two performance measures: the probability of a misleading signal (PMS) and the run length to a misleading signal (RLMS). We use stochastic ordering to analyze their monotonicity properties in terms of shifts in the parameters being monitored, and of changes in the autocorrelation parameter.
This seminar integrates a CAT examination

30/01/2009, 14:00 — 15:00 — Amphitheatre Pa2, Mathematics Building
Graciela Boente, Universidad de Buenos Aires and CONICET, Argentina

Robust tests in generalized partial linear models

In this talk we will first remind the robust procedures existing to estimate the regression parameter and the regression function under a generalized partial linear model. Based on them, we will describe how to construct a Wald type statistic to test hypothesis on the regression parameter and a robust test to decide if the regression function is linear. The asymptotic behavior of the test statistics and derived and results from a Monte Carlo study will be presented.

04/12/2008, 15:00 — 16:00 — Room P8, Mathematics Building, IST
Patrícia Gonçalves, Universidade do Minho, Portugal

Partial Differential Equations ans Stochastic Differential Equations Arising in Particle Systems

In this talk, I will introduce a classical example of Particle System: the Simple Exclusion Process. I will give the notion of hydrodynamic limit, which is a Law of Large Numbers for the empirical measure and I will explain how to derive from the microscopic dynamics between particles a partial differential equation describing the evolution of the density profile. For the Simple Exclusion Process, in the Symmetric case (p=1/2) we will get to the heat equation while in the Asymmetric case (p1/2) to the Burgers equation. Finally, I will introduce the Central Limit theorem for the empirical measure and the limiting process turns out to be a solution of a stochastic differential equation.

04/12/2008, 14:00 — 15:00 — Room P8, Mathematics Building, IST
Antonio Goméz-Corral, Department of Statistics OR, Faculty of Mathematics, Complutense University of Madrid, Spain

An Overview of Retrial Queues

The talk deals with a branch of queuing theory, retrial queuing systems, which is characterized by a basic assumption: a customer who cannot receive service (due to finite capacity of the system, balking, impatience, etc.) leaves the service area, but after some random delay returns to the system again to request service. As a result, repeated attempts for service from the pool of unsatisfied customers, called the orbit, are superimposed on the ordinary stream of arrivals of first attempts.

The talk is divided into three parts. Part I introduces the audience to the broad range of applications of retrial queues and compares retrial queues to standard queues with waiting line and queues with losses. We show that, though retrial queues are closely connected with standard queuing models, they possess unique and distinguishing characteristics. In Part II, we give a survey of main results for the main M/G/1 and M/M/c retrial queues. We also present the analysis of descriptors arising from the idiosyncrasies of the retrial feature. Part III uses the matrix-analytic formalism to analyze a selected number of retrial queues with underlying structured Markov chains.

07/11/2008, 15:00 — 16:00 — Amphitheatre Pa2, Mathematics Building
Vladas Pipiras, CEMAT / IST and University of North Carolina

Estimation of matrix rank: historical overview and more recent developments

esting for the rank of a matrix is an important problem arising in Statistics, Econometrics and other areas. It is of interest, for example, in reduced rank regression, cointegration analysis and other applications. In this talk, we will review past efforts in addressing this problem and discuss more recent developments concerning testing for the rank in symmetric matrices. (The latter part is based on joint work with Stephen Donald and Natercia Fortuna.)
This talk is part of the UTL Probability and Statistics Seminar

04/07/2008, 14:00 — 15:00 — Room P3, Mathematics Building, IST
Cláudia Nunes, CEMAT e Departamento de Matemática, Instituto Superior Técnico

Análise Probabilística de Opções Reais

A análise de opções reais, muito em voga nos meios financeiros actuais, lida com a necessidade premente de prever preços futuros de opções (de compra ou venda) num cenário aleatório, de forma a estabelecer preços contractuais justos para ambas as partes. Neste contexto a componente estocástica tem um papel relevante, que será explorado nesta apresentação. Veremos como o movimento Browniano, por exemplo, é indispensável na análise em horizonte finito de produtos financeiros, e como no dia a dia de "traders" especializados aparecem integrais estocásticos.

20/06/2008, 14:00 — 15:00 — Room P3, Mathematics Building, IST
Carlos Daniel Paulino, Departamento de Matemática, Instituto Superior Técnico

Análise bayesiana de privação das famílias portuguesas

Neste trabalho pretende-se analisar multidimensionalmente a pobreza das famílias portuguesas considerando quatro dimensões de bem-estar — Habitação, Bens de Conforto, Capacidade Económica e Redes de Sociabilidade — com base no Painel Europeu de Agregados Domésticos Privados do Eurostat. Propõe-se uma abordagem em várias etapas permitindo uma análise parcial e global da privação, recorrendo-se, para tal, à análise de modelos bayesianos de classes latentes através do método Monte Carlo via Cadeias de Markov. Os resultados obtidos evidenciam uma melhoria substancial no bem-estar das famílias entre 1995 e 2001. As dimensões Capacidade Económica e Redes de Sociabilidade são as que mais contribuem para a situação de privação das famílias.

30/05/2008, 14:00 — 15:00 — Room P3, Mathematics Building, IST
Manuela Souto de Miranda, Departamento de Matemática, Universidade de Aveiro

Erros nos regressores: realismo, desconforto e desafios

Os modelos de regressão constituem uma das ferramentas mais usadas pelos utilizadores da Estatística — por vezes ignorando que as variáveis são medidas com erro. Que fazer quando um regressor contém erros? Os modelos com erros-nas-variáveis dão a resposta. São uma extensão dos modelos de regressão, que disponibiliza uma descrição mais realista dos fenómenos em análise. Apesar das aparentes vantagens da modelação, os modelos com erros-nas-variáveis não têm tido a procura dos seus congéneres, nem a mesma aceitação. Porque será? Nesta apresentação procura-se responder às questões anteriores, divulgando esta família de modelos, comparando-os com os modelos de regressão, apontando os métodos a usar, as vantagens e os desafios associados à sua aplicação.

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