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Research Areas : Spatial Statistics and Machine
Learning

I
am a Professor in the Department of Computer Science,
Universidade
Federal de Minas Gerais (UFMG) located in Belo Horizonte, Brazil. I
received my Ph.D. in Statistics
in 1994 from the University
of
Washington, Seattle, USA, having Peter Guttorp
as advisor. From 1994 to 2011, I worked in
the Department of
Statistics, UFMG. I moved to my current
position in September 2011. I prefer to work in
applied problems where the stochastic
nature of the problem requires probabilistic and statistical
modeling. My main application areas have been
epidemiology social issues,
actuarial risk, and demographic analysis. Due to my recent move to the
computer science department, I have been adding new application areas
to my work. This interdisciplinary work
in
turn shape my methodological research in statistics and machine
learning.

My
current research is focused on the development of new
algorithms and statistical methods to analyse spatial
and space-time
data. I am primarily concerned with the spatial analysis of risk
appearing in many fields such as
epidemiological surveillance, geosensor networks, enviornmental
problems, marketing, spatially variable risk spatially
variable, among many
others. The computer revolution, still going on, have
made it possible to manage huge space-time
databases. The
extraction of interesting patterns from these massive databases create
new, challenging, and interesting problems that
require
creative algorithmic, statistical and probabilistic solutions.

Posted
on February 06, 2014 by Renato

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Recent work with

Erica Rodrigues,
a faculty at the Universidade Federal de Ouro Preto, appeared
recently in the
Journal of Multivariate Analysis. In Bayesian disease mapping, one
needs to specify a neighborhood structure to make inference about the
underlying geographical relative risks. We propose a model in which the
neighborhood structure is part of the parameter space. We retain the
Markov property of the typical Bayesian spatial models: given the
neighborhood graph, disease rates follow a conditional autoregressive
model. However, the neighborhood graph itself is a parameter that also
needs to be estimated. We investigate the theoretical properties of our
model. In particular, we investigate carefully the prior and posterior
covariance matrix induced by this random neighborhood structure,
providing interpretation for each element ofthese matrices.

Posted on February 06, 2013 by Renato

This
is another paper just accepted (Feb 2013) at the Journal of
Multivariate Analysis, a joint work with Ivair Ramos Silva, based on
his PhD dissertation at UFMG. The abstract reads as follows:

Conventional Monte Carlo tests require the simulation of $m$ independent

copies from the test statistic U under the null hypothesis $H_0$. The execution

time of these procedures can be substantially reduced by sequentially

monitoring the simulations. The literature has evaluated the properties

of specific sequential Monte Carlo designs to perform hypothesis tests implementations

by using restrictions on the probability distribution of

the p-value. Such restrictions are used to bound both the resampling risk,

the probability that the accept/reject decision is different from the decision

from the exact test, and the expected execution time. The application of

the main proposals in the literature depends on specific algorithms and its

power for finite number of simulations were not explored by its authors.

This paper develops a generalized sequential Monte Carlo test that includes

the main previous proposals and that allows an analytical treatment of the

power and the expected execution time. These results are valid for any test statistic.

We define the sequential risk, the probability that the accept/reject decision is

different from the decision from the conventional Monte Carlo test, and construct

an optimal sequential procedure which minimizes the expected number of simulations

within a large set of designs. We also bound the resampling risk by consider

a large class of p-value distributions.

My second submission to a CS conference.

Accepted at the

23rd International World-Wide Web Conference (WWW 2013).

Authors: Pedro Olmo Vaz de Melo, Christos Faloutsos, Renato Assuncao and Antonio Loureiro.