Eufrásio de Andrade Lima Neto

A new method to fit a linear regression model for interval-valued data

This paper introduces a new approach to fit a linear regression model on interval-valued data. Each example of the learning set is described by a feature vector where each feature value is an interval. In the proposed approach, it is fitted two linear regression models, respectively, on the mid-point and range of the interval values assumed by the variables on the learning set. The prediction of the lower and upper bound of the interval value of the dependent variable is accomplished from its mid-point and range which are estimated from the fitted linear regression models applied to the mid-point and range of each interval values of the independent variables. The evaluation of the proposed prediction method is based on the estimation of the average behaviour of root mean squared error and of the determination coefficient in the framework of a Monte Carlo experience in comparison with the method proposed by Billard and Diday [3].

Bivariate symbolic regression models for interval-valued variables

Interval-valued variables have become very common in data analysis. Up until now, symbolic regression mostly approaches this type of data from an optimization point of view, considering neither the probabilistic aspects of the models nor the nonlinear relationships between the interval response and the interval predictors. In this article, we formulate interval-valued variables as bivariate random vectors and introduce the bivariate symbolic regression model based on the generalized linear models theory which provides much-needed exibility in practice. Important inferential aspects are investigated. Applications to synthetic and real data illustrate the usefulness of the proposed approach.

Univariate and multivariate linear regression methods to predict interval-valued features

This paper introduces two new approaches to fit a linear regression model on interval-valued data Each example of the learning set is described by a feature vector where each feature value is an interval In the first proposed approach, it is fitted two independent linear regression models, respectively, on the mid-point and range of the interval values assumed by the variables on the learning set In the second approach, is fitted a multivariate linear regression models on these mid-point and range The prediction of the lower and upper bound of the interval value of the dependent variable is accomplished from its mid-point and range which are estimated from the fitted linear regression models applied to the mid-point and range of each interval values of the independent variables The evaluation of the proposed prediction methods is based on the average behavior of the root mean squared error and the determination coefficient in the framework of a Monte Carlo experiment in comparison with the method proposed by Billard and Diday [2].

Applying constrained linear regression models to predict interval-valued data

Billard and Diday [2] were the first to present a regression method for interval-value data. De Carvalho et al [5] presented a new approach that incorporated the information contained in the ranges of the intervals and that presented a better performance when compared with the Billard and Diday method. However, both methods do not guarantee that the predicted values of the lower bounds (ŷLi) will be lower than the predicted values of the upper bounds (ŷUi). This paper presents two approaches based on regression models with inequality constraints that guarantee the mathematical coherence between the predicted values ŷLi and ŷUi. The performance of these approaches, in relation with the methods proposed by Billard and Diday [2] and De Carvalho et al [2], will be evaluated in framework of Monte Carlo experiments.

Linear Regression Methods to Predict Interval-Valued Data

This paper introduce a new criterion and two new linear regression methods to predict interval-valued data. The proposed approaches consist in a new point of view to study the relationship between the midpoints and the ranges of the interval-valued variables. The evaluation of the proposed prediction methods is based on the average behaviour of the root mean squared error and the square of the correlation coefficient in the framework of a Monte Carlo experiment in comparison with the method proposed by [3].

Condições Sensíveis à Atenção Primária: uma revisão descritiva dos resultados da produção acadêmica brasileira

O estudo propoe-se descrever e analisar os resultados da producao academica brasileira sobre Internacoes por Condicoes Sensiveis a Atencao Primaria. Revisao descritiva e analitica realizada em artigos cientificos de Portais de Periodicos. Vinte e um artigos integraram o estudo, os quais foram subdivididos segundo a abrangencia territorial, sendo 9,6% nacional, 4,8% do Nordeste, 9,6% do Centro-Oeste, 33,3% do Sul e 42,7% do Sudeste. As Internacoes por Causas Sensiveis a Atencao Primaria, apesar de se apresentarem altas em alguns estados e/ou cidades isoladas, sofrem uma tendencia para estabilizacao e reducao nas diferentes regioes brasileiras.

Edentulismo, Uso e Necessidade de Prótese e Fatores Associados em Município do Nordeste Brasileiro

Resumen pt: Introducao: No Brasil, a populacao idosa cresce com o aumento da expectativa de vida constituindo uma responsabilidade para os gestores publicos na persp...

Regression model for interval-valued variables based on copulas

In real problems, it is usual to have the available data presented as intervals. Therefore, different approaches have been proposed to obtain a regression model for this new type of data. In this paper, we represent the interval-valued response variable as a bivariate random vector and we consider the copulas theory to propose a general bivariate distribution for Z, creating a more flexible random component to the model. Inference techniques and a residual definition based on deviance are considered, as well as applications to synthetic and real data sets that demonstrate the usefulness of the proposed approach. The new method is also compared with other methods reported in the literature.In real problems, it is usual to have the available data presented as intervals. Therefore, different approaches have been proposed to obtain a regression model for this new type of data. In this paper, we represent the interval-valued response variable as a bivariate random vector and we consider the copulas theory to propose a general bivariate distribution for Z , creating a more flexible random component to the model. Inference techniques and a residual definition based on deviance are considered, as well as applications to synthetic and real data sets that demonstrate the usefulness of the proposed approach. The new method is also compared with other methods reported in the literature.

A pattern classifier for interval-valued data based on multinomial logistic regression model

Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. This paper introduces a multinomial logistic regression method for interval-valued data in order to classify items described by interval-valued variables into a pre-defined number of a priori classes. Applications of the proposed approach on real as well as synthetic interval-valued data sets showed the usefulness of this approach.

Bivariate Generalized Linear Model for Interval-Valued Variables

Current symbolic regression methods visualize problems from an optimization point of view and do not consider the probabilistic aspects related to regression models. In this paper, we present the bivariate generalized linear model (BGLM) proposed by Iwasaki and Tsubaki [5] in the context of interval-valued data sets. Important aspects related to the BGLM that remain open or can be improved will be considered. The performance of this new approach in relation to symbolic regression methods proposed by Billard and Diday [1] and Lima Neto and De Carvalho [7] will be considered through real interval data sets.