Browsing by Author "Galea Rojas, Manuel Jesús"
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- ItemAddressing non-normality in multivariate analysis using the t-distribution(2023) Osorio, Felipe; Galea Rojas, Manuel Jesús; Henríquez, Claudio; Arellano Valle, Reinaldo BorisThe main aim of this paper is to propose a set of tools for assessing non-normality taking into consideration the class of multivariate t-distributions. Assuming second moment existence, we consider a reparameterized version of the usual t distribution, so that the scale matrix coincides with covariance matrix of the distribution. We use the local influence procedure and the Kullback–Leibler divergence measure to propose quantitative methods to evaluate deviations from the normality assumption. In addition, the possible non-normality due to the presence of both skewness and heavy tails is also explored. Our findings based on two real datasets are complemented by a simulation study to evaluate the performance of the proposed methodology on finite samples.
- ItemAnalysis of local influence in geostatistics using Student's t-distribution(2014) Assumpcao, R.; Uribe Opazo, M.; Galea Rojas, Manuel Jesús
- ItemAssessment of local influence for the analysis of agreement(2019) Leal, Carla; Galea Rojas, Manuel Jesús; Osorio, Felipe
- ItemBayesian inference for the pairwise probability of agreement using data from several measurement systems(Taylor & Francis Inc, 2021) Castro, Mario de; Galea Rojas, Manuel JesúsThis article deals with Bayesian inference in the comparison of measurement systems. Agreement between two systems can be evaluated using data from several measurement systems and using only data from the two systems being compared. With a measurement error model for replicated observations and the probability of agreement to compare measurement systems, we develop methods to compare measurement systems with either homoscedastic or heteroscedastic measurement errors under the Bayesian paradigm via Markov chain Monte Carlo methods. A graphical tool is described to check model adequacy. The methodology developed in the article is illustrated using a real dataset and through simulations.
- ItemBayesian inference in measurement error models for replicated data(2013) De Castro, Mario; Bolfarine, Heleno; Galea Rojas, Manuel Jesús
- ItemBirnbaum-Saunders quantile regression and its diagnostics with application to economic data(2021) Sánchez, L.; Leiva, Víctor; Galea Rojas, Manuel Jesús; Saulo, H.
- ItemBirnbaum-Saunders quantile regression models with application to spatial data(2020) Sánchez, Luis; Leiva, Víctor; Galea Rojas, Manuel Jesús; Saulo, HeltonIn the present paper, a novel spatial quantile regression model based on the Birnbaum-Saunders distribution is formulated. This distribution has been widely studied and applied in many fields. To formulate such a spatial model, a parameterization of the multivariate Birnbaum-Saunders distribution, where one of its parameters is associated with the quantile of the respective marginal distribution, is established. The model parameters are estimated by the maximum likelihood method. Finally, a data set is applied for illustrating the formulated model.
- ItemCase-deletion diagnostics for spatial linear mixed models(2018) De Bastiani, Fernanda; Uribe Opazo, M. A.; Galea Rojas, Manuel Jesús; Cysneiros, A. H. M. A.
- ItemDiagnostics in Birnbaum-Saunders accelerated life models with an application to fatigue data(2014) Leiva, V.; Rojas, E.; Galea Rojas, Manuel Jesús; Sanhueza, A.
- ItemElliptical linear mixed models with a covariate subject to measurement error(2020) Borssoi, J. A.; Paula, G. A.; Galea Rojas, Manuel Jesús
- ItemFitting time-varying parameters to astronomical time series(2022) Soto Vásquez, Darlin Macarena; Motta, Giovanni; Galea Rojas, Manuel Jesús; Pontificia Universidad Católica de Chile. Facultad de Matemáticas
- ItemGaussian spatial linear model of soybean yield using bootstrap methods(2018) Dalposso, Gustavo H.; Uribe-Opazo, Miguel A.; Johann, Jerry A.; Galea Rojas, Manuel Jesús; De Bastiani, Fernanda
- ItemGaussian spatial linear models with repetitions: An application to soybean productivity(2017) De Bastiani, F.; Galea Rojas, Manuel Jesús; Cysneiros, A.; Uribe, M.
- ItemGeneralized tobit models : diagnostics and application in econometrics(2018) Barros, Michelli; Galea Rojas, Manuel Jesús; Leiva, Víctor; Santos Neto, Manoel
- ItemGeostatistical modeling of soybean yield and soil chemical attributes using spatial bootstrap(2019) Dalposso, Gustavo H.; Uribe-Opazo, Miguel A.; Johann, Jerry A.; Bastiani, Fernanda de; Galea Rojas, Manuel JesúsThe goal of this study was to use the spatial bootstrap method to model the spatial dependence structure of soybean yield and soil chemical attributes in an agricultural area. The study involved developing confidence intervals in probability plots to determine the probability distributions assumed by the data; determine the empirical distributions of the semivariances and model parameters, allowing to obtain statistics and confidence intervals; and to construct maps for the variables. The quantile-quantile plots indicated that the data follows a normal distribution. The confidence intervals for the semivariances helped to model the spatial dependence structure, and the descriptive statistics of the bootstrap replicates of the model parameters allowed to test the consistency of the estimates. The soil chemical attributes (calcium, potassium, and organic matter) were at levels suitable for soybean cultivation. However, the pH was below the ideal range in most of the study area, and water stress during cultivation decreased the mean yield. Therefore, according to the results, a recommendation to the farmer is to correct the soil pH to increase the yield.
- ItemGlobal and local diagnostic analytics for a geostatistical model based on a new approach to quantile regression(Springer, 2020) Leiva, Victor; Sánchez, Luis; Galea Rojas, Manuel Jesús; Saulo, HeltonData with spatial dependence are often modeled by geoestatistical tools. In spatial regression, the mean response is described using explanatory variables with georeferenced data. This modeling frequently considers Gaussianity assuming the response follows a symmetric distribution. However, when this assumption is not satisfied, it is useful to suppose distributions with the same asymmetric behavior of the data. This is the case of the Birnbaum-Saunders (BS) distribution, which has been considered in different areas and particularly in environmental sciences due to its theoretical arguments. We propose a geostatistical model based on a new approach to quantile regression considering the BS distribution. Global and local diagnostic analytics are derived for this model. The estimation of model parameters and its local influence are conducted by the maximum likelihood method. Global influence is based on the Cook distance and it is compared to local influence, in both cases to detect influential observations, whose detection and removal can modify the conclusions of a study. We illustrate the proposed methodology applying it to environmental data, which shows this situation changing the conclusions after removing potentially influential observations. A comparison with Gaussian spatial regression is conducted.
- ItemGold Standard in selection of rainfall forecasting models for soybean crops region(Southern Cross Publishing, 2022) Oliveira, Marcio Paulo de; Uribe-Opazo, Miguel Ángel; Galea Rojas, Manuel Jesús; Johann, Jerry AdrianiRainfall data forecasting is essential in agricultural sciences due to impacts caused by water excess or deficit on crop growth. Our study aimed to develop a method to select rainfall forecast models using references with negligible error denoted as the gold standard. To this end, we used forecasting models from national centers such as Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Center for Environmental Prediction (NCEP), and Center for Weather Forecasting and Climate Studies (CPTEC). The study area comprised the western mesoregion of Paraná State (Brazil), and data were gathered from October to March between the soybean crop seasons of 2010/2011 and 2015/2016. Ten-day period clusters, corresponding to 240 h forecasts in the centers, were used to assess agreement with the gold standard. Our results showed that forecasting center selection must be based on rainfall value ranges and geographic locations. Selection according to the highest agreement with the gold standard was estimated at 76.9% for range 1 in CPTEC, 38.5% for range 2 and 4 in ECMWF, and 38.5% for range 3 in NCEP. In conclusion, the proposed method was efficient in selecting forecasting centers in areas of interest.
- ItemInference in a structural heteroskedastic calibration model(2015) De Castro, M.; Galea Rojas, Manuel Jesús
- ItemInference in multivariate regression models with measurement errors(2023) Sandoval Moreno, Gabriela; Galea Rojas, Manuel Jesús; Arellano Valle, Reinaldo BorisMultivariate regression models are helpful in many fields. However, independent variables (covariates or predictors) could be measured with error. That implies the necessity of considering a new kind of model called Multivariate Regression Models with Measurement Error (MRMMEs). This paper aims to carry out a statistical analysis of these models. We include estimation, hypothesis testing, model assessment, and influence diagnostics. Furthermore, besides considering the classical assumption of the normal distribution, we use maximum likelihood for the whole inference process. Finally, we study the developed approach's performance through simulation experiments and re-analyze the human lung function dataset presented in the literature to illustrate the methodology.
- ItemInfluence Assessment in an Heteroscedastic Errors-in-Variables Model(TAYLOR & FRANCIS INC, 2012) Galea Rojas, Manuel Jesús; Castro, Mario deThe main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.
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