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Utilization of public health centres in Portugal: effect of time costs and other determinants. Finite mixture models applied to truncated samples

Lourenço, Óscar Domingos; Ferreira, Pedro Lopes
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.91%
The impact of time costs on the utilization of medical care has been a subject of theoretical and empirical research since the early 1970s.The main goal of this paper is to show the effect of time costs on the number of visits to general practitioners (GP) in Portuguese public health centres. We measured the elasticity of primary health care utilization relative to the total time spent in the health centre and relative to travel time. We also provided evidence regarding the impact of an appointment delay on the utilization of public GP services.Our data resulted from the application of an endogenous sampling scheme, resulting in a truncated-at-zero data set. To model our dependent variable, number of visits, and accounting for the truncated nature of the data we used a finite mixture model specification.The data were obtained from the most recent implementation in Portugal of the 2003/2004 Europep Survey.The two-component negative binomial II finite mixture model led to the identification of two different latent classes of health centre users: a low-users class that comprises 88% of patients with an estimated utilization mean of 4.3 GP visits per year and a frequent-users class with an estimated utilization mean of 11.1 visits for the remaining 12% of the population.We failed to find any statistically significant elasticity of time cost utilization...

The impact of non-monetary factors on the primary care utilization in Portugal. Finite mixture models applied to on-site and truncated samples

Lourenço, Óscar Domingos; Ferreira, Pedro Lopes
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Conferência ou Objeto de Conferência
Português
Relevância na Pesquisa
75.89%
In the Portuguese National Health Service (NHS) patients have to pay a co-payment of 2€ to visit a GP in the health centres. Therefore, the monetary price associated to each visit is low and, with a high probability, is not a factor that affects the utilization of consultations in health centres. On the other hand, in any health system in which the monetary cost to consume medical care is very low other kind of costs can emerge as determinants of medical care utilization. The Portuguese NHS suffers from several time-related inefficiencies and so, the non-monetary form of co-payment is a non negligible reality. With data in our database we have concluded that the average waiting time to visit a GP is approximately 9 days. Moreover, the average waiting time in the waiting room for a consultation is approximately 1 hour. Therefore, this study aims at analysing the impact of non-monetary factors on the utilization of public GPs. This study can be useful for policy making, as well as for econometric reasons. In the other hand, sometimes the empirical researcher faces non-random samples. So, modelling based on the assumption that we have a simple random sample can be inappropriate and misleading. In this research we face this same situation. Our data resulted from the application of two endogenous sampling schemes: a sample collected on-site and a truncated sample. Therefore each sampling scheme generates a selected sample. Thus...

Análise de carteiras em tempo discreto ; Discrete time portfolio analysis

Kato, Fernando Hideki
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 14/04/2004 Português
Relevância na Pesquisa
45.98%
Nesta dissertação, o modelo de seleção de carteiras de Markowitz será estendido com uma análise em tempo discreto e hipóteses mais realísticas. Um produto tensorial finito de densidades Erlang será usado para aproximar a densidade de probabilidade multivariada dos retornos discretos uniperiódicos de ativos dependentes. A Erlang é um caso particular da distribuição Gama. Uma mistura finita pode gerar densidades multimodais não-simétricas e o produto tensorial generaliza este conceito para dimensões maiores. Assumindo que a densidade multivariada foi independente e identicamente distribuída (i.i.d.) no passado, a aproximação pode ser calibrada com dados históricos usando o critério da máxima verossimilhança. Este é um problema de otimização em larga escala, mas com uma estrutura especial. Assumindo que esta densidade multivariada será i.i.d. no futuro, então a densidade dos retornos discretos de uma carteira de ativos com pesos não-negativos será uma mistura finita de densidades Erlang. O risco será calculado com a medida Downside Risk, que é convexa para determinados parâmetros, não é baseada em quantis, não causa a subestimação do risco e torna os problemas de otimização uni e multiperiódico convexos. O retorno discreto é uma variável aleatória multiplicativa ao longo do tempo. A distribuição multiperiódica dos retornos discretos de uma seqüência de T carteiras será uma mistura finita de distribuições Meijer G. Após uma mudança na medida de probabilidade para a composta média...

Modelos de mistura de distribuições na segmentação de imagens SAR polarimétricas multi-look; Multi-look polarimetric SAR image segmentation using mixture models

Horta, Michelle Matos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 04/06/2009 Português
Relevância na Pesquisa
55.9%
Esta tese se concentra em aplicar os modelos de mistura de distribuições na segmentação de imagens SAR polarimétricas multi-look. Dentro deste contexto, utilizou-se o algoritmo SEM em conjunto com os estimadores obtidos pelo método dos momentos para calcular as estimativas dos parâmetros do modelo de mistura das distribuições Wishart, Kp ou G0p. Cada uma destas distribuições possui parâmetros específicos que as diferem no ajuste dos dados com graus de homogeneidade variados. A distribuição Wishart descreve bem regiões com características mais homogêneas, como cultivo. Esta distribuição é muito utilizada na análise de dados SAR polarimétricos multi-look. As distribuições Kp e G0p possuem um parâmetro de rugosidade que as permitem descrever tanto regiões mais heterogêneas, como vegetação e áreas urbanas, quanto regiões homogêneas. Além dos modelos de mistura de uma única família de distribuições, também foi analisado o caso de um dicionário contendo as três famílias. Há comparações do método SEM proposto para os diferentes modelos com os métodos da literatura k-médias e EM utilizando imagens reais da banda L. O método SEM com a mistura de distribuições G0p forneceu os melhores resultados quando os outliers da imagem são desconsiderados. A distribuição G0p foi a mais flexível ao ajuste dos diferentes tipos de alvo. A distribuição Wishart foi robusta às diferentes inicializações. O método k-médias com a distribuição Wishart é robusto à segmentação de imagens contendo outliers...

Novas empresas e criação de emprego: dois ensaios com modelos de mistura

Pontes, Leandro Manuel Branco Pequito
Fonte: Instituto Universitário de Lisboa Publicador: Instituto Universitário de Lisboa
Tipo: Dissertação de Mestrado
Publicado em //2008 Português
Relevância na Pesquisa
55.83%
Mestrado em Prospecção e Análise de Dados / JEL: C25, L26, M13, J23; As novas empresas e o sector das pequenas e médias empresas constituem vectores fundamentais para o desenvolvimento das economias ocidentais. Neste contexto, o empresário responsável pela constituição de uma nova empresa desempenha um papel importante, com impacto no desempenho da economia. Com base em dados recolhidos para a economia portuguesa sobre os empresários que constituíram empresas em 2002, procede-se à construção de uma tipologia de empresários baseada nas suas motivações. Os resultados indicam a existência de três segmentos, com motivações e perfis distintos. As críticas a trabalhos anteriores são acolhidas neste estudo, através da selecção das variáveis de segmentação e da técnica estatística escolhida. O modelo de mistura com variáveis concomitantes utilizado inclui num único modelo probabilístico o processo de segmentação e caracterização dos segmentos, ao contrário do processo tradicional em duas etapas. Numa segunda fase, procede-se à avaliação do efeito das características do empresário sobre a criação de emprego, com base num modelo de mistura de regressões de Poisson. Este modelo incorpora dois aspectos inovadores: considera a população de empresários como heterogénea e admite para a variável Emprego uma distribuição de Poisson. Os resultados indicam a existência de três segmentos de empresários e sugerem efeitos positivos associados à experiência de gestão e de constituição de empresas...

Um agrupamento de turistas: modelação adequada?

Prata, Joel Adilson da Costa
Fonte: Instituto Universitário de Lisboa Publicador: Instituto Universitário de Lisboa
Tipo: Dissertação de Mestrado
Publicado em //2011 Português
Relevância na Pesquisa
55.84%
Mestrado em Prospecção e Análise de Dados; A zona turística rural no norte de Portugal é uma das áreas escolhidas por alguns turistas que visitam o nosso País. A auto-imagem desses turistas pode aferir-se mediante atributos capazes de caracterizar também o destino visitado. Neste trabalho aplica-se a metodologia de estimação de modelos de mistura finita para constituição e caracterização dos segmentos de turistas com base em atributos de autoimagem. Na modelação atende-se ao tipo de escala – Semântica Diferencial – de dois modos distintos: 1 – as variáveis base são modeladas como métricas e usada uma mistura de distribuições normais; 2 – as variáveis base são modeladas como ordinais e usada uma mistura de multinomiais. Aplica-se a metodologia de validação cruzada com o auxílio dos índices de concordância para comparar a estabilidade das soluções alternativas de agrupamento, com base na modelação das variáveis como métricas ou ordinais. No trabalho desenvolvido o agrupamento com base nas variáveis ordinais tem uma maior estabilidade. Este agrupamento é seleccionado e caracterizado.; The touristic rural zone in the north of Portugal is one of the areas chosen by some tourists who visit our Country. The auto-image of these tourists can be checked by means of attributes able to also characterize the visited destination. In this paper...

A mixture model approach to multiple testing for the genetic analysis of gene expression

Dalmasso, Cyril; Pickrell, Joseph; Tuefferd, Marianne; Génin, Emmanuelle; Bourgain, Catherine; Broët, Philippe
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Publicado em 18/12/2007 Português
Relevância na Pesquisa
55.74%
With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR, and the false non-discovery rate (FNR) in variance-component linkage analysis. Our parametric approach allows empirical estimation of an appropriate null distribution. The contribution of our model to estimation of FDR and related criteria is illustrated on the microarray expression profiles data set provided by the Genetic Analysis Workshop 15 Problem 1.

A RICIAN MIXTURE MODEL CLASSIFICATION ALGORITHM FOR MAGNETIC RESONANCE IMAGES

Roy, Snehashis; Carass, Aaron; Bazin, Pierre-Louis; Prince, Jerry L.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //2009 Português
Relevância na Pesquisa
55.82%
Tissue classification algorithms developed for magnetic resonance images commonly assume a Gaussian model on the statistics of noise in the image. While this is approximately true for voxels having large intensities, it is less true as the underlying intensity becomes smaller. In this paper, the Gaussian model is replaced with a Rician model, which is a better approximation to the observed signal. A new classification algorithm based on a finite mixture model of Rician signals is presented wherein the expectation maximization algorithm is used to find the joint maximum likelihood estimates of the unknown mixture parameters. Improved accuracy of tissue classification is demonstrated on several sample data sets. It is also shown that classification repeatability for the same subject under different MR acquisitions is improved using the new method.

Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging

Tohka, Jussi; Krestyannikov, Evgeny; Dinov, Ivo D.; Graham, Allan MacKenzie; Shattuck, David W.; Ruotsalainen, Ulla; Toga, Arthur W.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /05/2007 Português
Relevância na Pesquisa
55.75%
Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging...

A hierarchical finite mixture model that accommodates zero-inflated counts, non-independence, and heterogeneity

Morgan, Charity J.; Lenzenweger, Mark F.; Rubin, Donald B.; Levy, Deborah L.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.87%
A number of mixture modeling approaches assume both normality and independent observations. However, these two assumptions are at odds with the reality of many data sets, which are often characterized by an abundance of zero-valued or highly skewed observations as well as observations from biologically related (i.e., non-independent) subjects. We present here a finite mixture model with a zero-inflated Poisson regression component that may be applied to both types of data. This flexible approach allows the use of covariates to model both the Poisson mean and rate of zero-inflation and can incorporate random effects to accommodate non-independent observations. We demonstrate the utility of this approach by applying these models to a candidate endophenotype for schizophrenia, but the same methods are applicable to other types of data characterized by zero inflation and non-independence.

A Finite Mixture Survival Model to Characterize Risk Groups of Neuroblastoma

Hunsberger, Sally; Albert, Paul S.; London, Wendy B.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 15/04/2009 Português
Relevância na Pesquisa
55.82%
Neuroblastoma is a childhood cancer with patients experiencing heterogeneous survival outcomes despite aggressive treatment. Disease outcomes range from early death to spontaneous regression of the tumor followed by cure. Due to this heterogeneity, it is of interest to identify patients with similar types of neuroblastoma so that specific types of treatment can be developed. Oncologists are especially interested in identifying patients who will be cured so that the minimum amount of a potentially toxic treatment can be given to this group of patients. We analyze a large cohort of neuroblastoma patients and develop a finite mixture model that uses covariates to predict the probability of being in a cure group or other (one or more) risk groups. A prediction method is developed that uses the estimated probabilities to assign a patient to different risk groups. The robustness of the model and the prediction method is examined via simulation by looking at misclassification rates under misspecified models.

A hierarchical finite mixture model that accommodates zero-inflated counts, non-independence, and heterogeneity

Morgan, Charity J.; Lenzenweger, Mark F.; Rubin, Donald B.; Levy, Deborah L.
Fonte: Wiley-Blackwell Publicador: Wiley-Blackwell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
75.9%
A number of mixture modeling approaches assume both normality and independent observations. However, these two assumptions are at odds with the reality of many data sets, which are often characterized by an abundance of zero-valued or highly skewed observations as well as observations from biologically related (i.e., non-independent) subjects. We present here a finite mixture model with a zero-inflated Poisson regression component that may be applied to both types of data. This flexible approach allows the use of covariates to model both the Poisson mean and rate of zero inflation and can incorporate random effects to accommodate non-independent observations. We demonstrate the utility of this approach by applying these models to a candidate endophenotype for schizophrenia, but the same methods are applicable to other types of data characterized by zero inflation and non-independence.; Statistics

Behavioral differences in violence: the case of intra-group differences of paramilitaries and guerrillas in Colombia

Bassetti, Thomas; Caruso, Raul; Cortes, Darwin
Fonte: Facultad de Economía Publicador: Facultad de Economía
Tipo: info:eu-repo/semantics/workingPaper; info:eu-repo/semantics/publishedVersion Formato: application/pdf
Publicado em 23/09/2015 Português
Relevância na Pesquisa
55.75%
In most studies on civil wars, determinants of conflict have been hitherto explored assuming that actors involved were either unitary or stable. However, if this intra-group homogeneity assumption does not hold, empirical econometric estimates may be biased. We use Fixed Effects Finite Mixture Model (FE-FMM) approach to address this issue that provides a representation of heterogeneity when data originate from different latent classes and the affiliation is unknown. It allows to identify sub-populations within a population as well as the determinants of their behaviors. By combining various data sources for the period 2000-2005, we apply this methodology to the Colombian conflict. Our results highlight a behavioral heterogeneity in guerrilla’s armed groups and their distinct economic correlates. By contrast paramilitaries behave as a rather homogenous group.

A finite mixture model for genotype and environment interactions: Detecting latent population heterogeneity

Gillespie, Nathan A.; Neale, Michael C.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /06/2006 Português
Relevância na Pesquisa
65.96%
Approaches such as DeFries-Fulker extremes regression (LaBuda et al., 1986) are commonly used in genetically informative studies to assess whether familial resemblance varies as a function of the scores of pairs of twins. While useful for detecting such effects, formal modelling of differences in variance components as a function of pairs' trait scores is rarely attempted. We therefore present a finite mixture model which specifies that the population consists of latent groups which may differ in i) their means, and ii) the relative impact of genetic and environmental factors on within-group variation and covariation. This model may be considered as a special case of a factor mixture model, which combines the features of a latent class model with those of a latent trait model. Various models for the class membership of twin pairs may be employed, including additive genetic, common environment, specific environment or major locus (QTL) factors. Simulation results based on variance components derived from Turkheimer and colleagues (2003), illustrate the impact of factors such as the difference in group means and variance components on the feasibility of correctly estimating the parameters of the mixture model. Model-fitting analyses estimated group heritability as .49...

Finite mixture model of conditional dependencies modes to cluster categorical data

Marbac, Matthieu; Biernacki, Christophe; Vandewalle, Vincent
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/02/2014 Português
Relevância na Pesquisa
55.79%
We propose a parsimonious extension of the classical latent class model to cluster categorical data by relaxing the class conditional independence assumption. Under this new mixture model, named Conditional Modes Model, variables are grouped into conditionally independent blocks. The corresponding block distribution is a parsimonious multinomial distribution where the few free parameters correspond to the most likely modality crossings, while the remaining probability mass is uniformly spread over the other modality crossings. Thus, the proposed model allows to bring out the intra-class dependency between variables and to summarize each class by a few characteristic modality crossings. The model selection is performed via a Metropolis-within-Gibbs sampler to overcome the computational intractability of the block structure search. As this approach involves the computation of the integrated complete-data likelihood, we propose a new method (exact for the continuous parameters and approximated for the discrete ones) which avoids the biases of the \textsc{bic} criterion pointed out by our experiments. Finally, the parameters are only estimated for the best model via an \textsc{em} algorithm. The characteristics of the new model are illustrated on simulated data and on two biological data sets. These results strengthen the idea that this simple model allows to reduce biases involved by the conditional independence assumption and gives meaningful parameters. Both applications were performed with the R package \texttt{CoModes}

Document Classification Using a Finite Mixture Model

Li, Hang; Yamanishi, Kenji
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/05/1997 Português
Relevância na Pesquisa
65.97%
We propose a new method of classifying documents into categories. The simple method of conducting hypothesis testing over word-based distributions in categories suffers from the data sparseness problem. In order to address this difficulty, Guthrie et.al. have developed a method using distributions based on hard clustering of words, i.e., in which a word is assigned to a single cluster and words in the same cluster are treated uniformly. This method might, however, degrade classification results, since the distributions it employs are not always precise enough for representing the differences between categories. We propose here the use of soft clustering of words, i.e., in which a word can be assigned to several different clusters and each cluster is characterized by a specific word probability distribution. We define for each document category a finite mixture model, which is a linear combination of the probability distributions of the clusters. We thereby treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models. In order to accomplish this testing, we employ the EM algorithm which helps efficiently estimate parameters in a finite mixture model. Experimental results indicate that our method outperforms not only the method using distributions based on hard clustering...

Exact fit of simple finite mixture models

Tasche, Dirk
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.93%
How to forecast next year's portfolio-wide credit default rate based on last year's default observations and the current score distribution? A classical approach to this problem consists of fitting a mixture of the conditional score distributions observed last year to the current score distribution. This is a special (simple) case of a finite mixture model where the mixture components are fixed and only the weights of the components are estimated. The optimum weights provide a forecast of next year's portfolio-wide default rate. We point out that the maximum-likelihood (ML) approach to fitting the mixture distribution not only gives an optimum but even an exact fit if we allow the mixture components to vary but keep their density ratio fix. From this observation we can conclude that the standard default rate forecast based on last year's conditional default rates will always be located between last year's portfolio-wide default rate and the ML forecast for next year. As an application example, then cost quantification is discussed. We also discuss how the mixture model based estimation methods can be used to forecast total loss. This involves the reinterpretation of an individual classification problem as a collective quantification problem.; Comment: 16 pages...

A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model

Monti, Stefano; Cooper, Gregory F.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 23/01/2013 Português
Relevância na Pesquisa
65.93%
In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification performance, both in terms of accuracy and in terms of calibration of the estimated probabilities. The proposed classifier is obtained by superimposing a finite mixture model on the set of feature variables of a naive Bayes model. We present experimental results that compare the predictive performance on real datasets of the new classifier with the predictive performance of the NB classifier and the FM classifier.; Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

Choosing the number of clusters in a finite mixture model using an exact Integrated Completed Likelihood criterion

Bertoletti, Marco; Friel, Nial; Rastelli, Riccardo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.76%
The integrated completed likelihood (ICL) criterion has proven to be a very popular approach in model-based clustering through automatically choosing the number of clusters in a mixture model. This approach effectively maximises the complete data likelihood, thereby including the allocation of observations to clusters in the model selection criterion. However for practical implementation one needs to introduce an approximation in order to estimate the ICL. Our contribution here is to illustrate that through the use of conjugate priors one can derive an exact expression for ICL and so avoiding any approximation. Moreover, we illustrate how one can find both the number of clusters and the best allocation of observations in one algorithmic framework. The performance of our algorithm is presented on several simulated and real examples.; Comment: 23 pages, to appear in Metron

Efficiency in the Municipal Public Education Provision: An Analysis in three Stages of Brazilian Municipalities; Eficiência na Provisão de Educação Pública Municipal: Uma Análise em três Estágios dos Municípios Brasileiros

Gonçalves, Flávio de Oliveira; França, Marco Túlio Aniceto
Fonte: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade Publicador: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; Formato: application/pdf
Publicado em 04/06/2013 Português
Relevância na Pesquisa
55.75%
The article analyses what determines efficiency concerning with educational managementin Brazilian municipalities, as a result of decentralization process which happenedin 90s. The pieces of information were extracted from Censo Escolar, Prova Brasil Finbraand STN dataset for the 2005-year. We employ a methodology with three stages of whichthe first stage consists of using the SBM (slacks based measure) models in the efficiencyestimation for the use of the discretionary inputs. In addition to this, non-discretionaryinputs were controlled of which the result is a new efficiency index. Finally, we usethe finite mixture models to analyse the heterogeneity among the municipalities. Theresults showed that there is no uniformity in the demographic and political effects onthe efficiency of the quality of education on offer among the diversity of the Brazilianmunicipalities groups. If there is more democracy and funds such as Fundef, in general,it will increase the efficiency of municipal school administration.; O artigo busca analisar as características municipais que afetam a eficiência dos municípiosbrasileiros na gestão educacional, consequência do processo de descentralizaçãoeducacional na década de 90. As informações foram extraídas da Prova Brasil...