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Spectrophotometric determination of organic dye mixtures by using multivariate calibration
(Elsevier Science BvAmsterdamHolanda, 1998)
A Bayesian quantile binary regression approach to estimate payments for environmental services
(2017)
Stated preference approaches, such as contingent valuation, focus mainly on the estimation of the mean or median willingness to pay (WTP) for an environmental good. Nevertheless, these two welfare measures may not be ...
A Bayesian quantile binary regression approach to estimate payments for environmental services
(2017)
Stated preference approaches, such as contingent valuation, focus mainly on the estimation of the mean or median willingness to pay (WTP) for an environmental good. Nevertheless, these two welfare measures may not be ...
Bayesian logistic regression: An application for carbonisation damage in four wood species
(2021-01-01)
This paper aimed to measure the carbonization action in four wood species with different densities formed by a variety with global utilization, eucalyptus grandis, and other three native species from Brazil, cupiuba, ...
Modelos de regressão para resposta binária na presença de dados desbalanceados
(Universidade Federal de São CarlosUFSCarPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsCâmpus São Carlos, 2019-02-22)
In binary regression, imbalanced data result from the presence of values equal to zero (or one) in a proportion that is significantly greater than the corresponding real values of one (or zero). In this work, we evaluate ...
Evaluating Value-at-Risk models via Quantile regressions
(Fundação Getulio Vargas. Escola de Pós-graduação em Economia, 2008-09-04)
This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables to do this sacrifices too much information. However, most of the specification tests (also called backtests) ...
Classification of Parkinson's disease patients based on spectrogram using local binary pattern descriptors
(IOP Publishing, 2022)
Extreme learning machine is an algorithm that has shown a good performance
facing classi cation and regression problems. It has gained great acceptance by the scienti c
community due to the simplicity of the model and ...
Assessment of predictive models for binary outcomes: an empirical approach using operative death from cardiac surgery
(Wiley, 1994)
Predictive models in medical research have gained popularity among physicians as an important tool in medical decision making. Eight methodological strategies for creating predictive models are compared in a large, complex ...
Conditional vs Unconditional Quantile Regression Models: A Guide to Practitioners
(Pontificia Universidad Católica del PerúPE, 2022)