DSpace Communidade:
http://www.repositorio.ufop.br/handle/123456789/561
Wed, 22 Mar 2017 13:52:33 GMT2017-03-22T13:52:33ZTesting spatial cluster occurrence in maps equipped with environmentally defined structures.
http://www.repositorio.ufop.br/handle/123456789/7388
Título: Testing spatial cluster occurrence in maps equipped with environmentally defined structures.
Autor(es): Duczmal, Luiz; Tavares, Ricardo; Patil, Ganapati; Cançado, André Luiz Fernandes
Resumo: We propose a novel tool for testing hypotheses concerning the adequacy
of environmentally defined factors for local clustering of diseases, through the comparative
evaluation of the significance of the most likely clusters detected under maps
whose neighborhood structures were modified according to those factors. A multiobjective
genetic algorithm scan statistic is employed for finding spatial clusters in
a map divided in a finite number of regions, whose adjacency is defined by a graph
structure. This cluster finder maximizes two objectives, the spatial scan statistic and
the regularity of cluster shape. Instead of specifying locations for the possible clusters
a priori, as is currently done for cluster finders based on focused algorithms, we
alter the usual adjacency induced by the common geographical boundary between
regions. In our approach, the connectivity between regions is reinforced or weakened,
according to certain environmental features of interest associated with the map. We
build various plausible scenarios, each time modifying the adjacency structure on specific
geographic areas in the map, and run the multi-objective genetic algorithm for
selecting the best cluster solutions for each one of the selected scenarios. The statistical
significances of the most likely clusters are estimated through Monte Carlo simulations. The clusters with the lowest estimated p-values, along with their corresponding
maps of enhanced environmental features, are displayed for comparative
analysis. Therefore the probability of cluster detection is increased or decreased,
according to changes made in the adjacency graph structure, related to the selection of
environmental features. The eventual identification of the specific environmental conditions
which induce the most significant clusters enables the practitioner to accept or
reject different hypotheses concerning the relevance of geographical factors. Numerical
simulation studies and an application for malaria clusters in Brazil are presented.Fri, 01 Jan 2010 00:00:00 GMThttp://www.repositorio.ufop.br/handle/123456789/73882010-01-01T00:00:00ZTests for mean vectors in high dimension
http://www.repositorio.ufop.br/handle/123456789/7387
Título: Tests for mean vectors in high dimension
Autor(es): Maboudou-Tchao, Edgard M.; Silva, Ivair
Resumo: Traditional multivariate tests, Hotelling’s T 2 or Wilks , are designed for a test of the mean vector under the
condition that the number of observations is larger than the number of variables. For high-dimensional data, where the number
of features is nearly as large as or larger than the number of observations, the existing tests do not provide a satisfactory
solution because of the singularity of the estimated covariance matrix. In this article, we consider a test for the mean vector
of independent and identically distributed multivariate normal random vectors where the dimension is larger than or equal to
the number of observations. To solve this problem, we propose a modified Hotelling statistic. Simulation results show that the
proposed test is superior to other tests available in the literature. However, because we do not know the theoretical distribution
of this modified statistic, Monte Carlo methods were used to reach this conclusion. Instead of using conventional Monte Carlo
methods, which perform a fixed-number of simulations, we suggest using the sequential Monte Carlo test in order to decrease
the number of simulations needed to reach a decision. Simulation results show that the sequential Monte Carlo test is preferable
to a fixed-sample test, especially when using computationally intensive statistical methods.Tue, 01 Jan 2013 00:00:00 GMThttp://www.repositorio.ufop.br/handle/123456789/73872013-01-01T00:00:00ZAnálise espaço-temporal aplicada às ocorrências de hipertensão e diabetes nos municípios do estado de Minas Gerais.
http://www.repositorio.ufop.br/handle/123456789/7386
Título: Análise espaço-temporal aplicada às ocorrências de hipertensão e diabetes nos municípios do estado de Minas Gerais.
Autor(es): Pinto, Eliangela Saraiva Oliveira; Santos, Gerson Rodrigues dos; Oliveira, Fernando Luiz Pereira de
Resumo: Objetivando avaliar a distribuição espaço-temporal de hipertensão arterial e diabetes
mellitus nos municípios de Minas Gerais, entre 2002 a 2012, aplicou-se as técnicas de análise
espacial de dados de área, destacando-se a média móvel local, os coeficientes de autocorrelação
global de Moran e o índice local de Moran. Além disso, foram construídos mapas temáticos de
distribuição espacial, de autocorrelação local (Box Map) e de identificação de cluster, utilizando
a estatística Scan espaço-temporal. Verificou-se que há autocorrelação espacial para ambas as
variáveis, entre os municípios, em que apresentou coeficientes de Moran (global) positivos e
significativos para todos os anos estudados. Foi possível identificar, por meio da análise local,
agrupamento de municípios situados na parte sul do estado com maiores taxas de prevalência de
hipertensão e também de diabetes em todos os anos. Também identificou-se a ocorrência de
cluster significativo localizado no sul do estado para as duas variáveis analisadas. Conclui-se que
a análise espaço-temporal permitiu mapear e compreender a distribuição das taxas de hipertensão
e diabetes no estado de Minas Gerais. Estudos como este podem fornecer informações para que
os serviços de saúde possam selecionar os principais locais com taxas altas e propor ações de
controle.Wed, 01 Jan 2014 00:00:00 GMThttp://www.repositorio.ufop.br/handle/123456789/73862014-01-01T00:00:00ZInternal cohesion and geometric shape of spatial clusters.
http://www.repositorio.ufop.br/handle/123456789/7385
Título: Internal cohesion and geometric shape of spatial clusters.
Autor(es): Duarte, Anderson Ribeiro; Duczmal, Luiz; Ferreira, Sabino José; Cançado, André Luiz Fernandes
Resumo: The geographic delineation of irregularly shaped spatial clusters is an ill
defined problem. Whenever the spatial scan statistic is used, some kind of penalty
correction needs to be used to avoid clusters’ excessive irregularity and consequent
reduction of power of detection. Geometric compactness and non-connectivity regularity
functions have been recently proposed as corrections. We present a novel internal
cohesion regularity function based on the graph topology to penalize the presence of
weak links in candidate clusters. Weak links are defined as relatively unpopulated
regions within a cluster, such that their removal disconnects it. By applying this weak
link cohesion function, the most geographically meaningful clusters are sifted through
the immense set of possible irregularly shaped candidate cluster solutions. A multiobjective
genetic algorithm (MGA) has been proposed recently to compute the Paretosets
of clusters solutions, employing Kulldorff’s spatial scan statistic and the geometric
correction as objective functions. We propose novel MGAs to maximize the spatial
scan, the cohesion function and the geometric function, or combinations of these
functions. Numerical tests show that our proposed MGAs has high power to detect
elongated clusters, and present good sensitivity and positive predictive value. The statistical
significance of the clusters in the Pareto-set are estimated through Monte Carlo
simulations. Our method distinguishes clearly those geographically inadequate clusters
which are worse from both geometric and internal cohesion viewpoints. Besides, a certain degree of irregularity of shape is allowed provided that it does not impact
internal cohesion. Our method has better power of detection for clusters satisfying
those requirements. We propose a more robust definition of spatial cluster using these
concepts.Fri, 01 Jan 2010 00:00:00 GMThttp://www.repositorio.ufop.br/handle/123456789/73852010-01-01T00:00:00Z