Statistics and Probability, Statistics, Probability and Uncertainty, Education
4
Scopus Publications
Scopus Publications
A GENERALIZED HECKMAN MODEL WITH VARYING SAMPLE SELECTION BIAS AND DISPERSION PARAMETERS Fernando de Souza Bastos, Wagner Barreto-Souza, Marc G. Genton Statistica Sinica, 2022 Many proposals have emerged as alternatives to the Heckman selection model, mainly to address the non-robustness of its normal assumption. The 2001 Medical Expenditure Panel Survey data is often used to illustrate this non-robustness of the Heckman model. \nIn this paper, we propose a generalization of the Heckman sample selection model by allowing the sample selection bias and dispersion parameters to depend on covariates. We show that the non-robustness of the Heckman model may be due to the assumption of the constant sample selection bias parameter rather than the normality assumption. Our proposed methodology allows us to understand which covariates are important to explain the sample selection bias phenomenon rather than to only form conclusions about its presence. Further, \nour approach may attenuate the non-identifiability and multicollinearity problems faced by the existing sample selection models. We explore the inferential aspects of the maximum likelihood estimators (MLEs) for our proposed generalized Heckman model. More specifically, we show that this model satisfies some regularity conditions such that it ensures consistency.
Birnbaum–Saunders sample selection model Fernando de Souza Bastos, Wagner Barreto-Souza Journal of Applied Statistics, 2021 The sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the outcome and the selection rule. In a pioneering work, J. Heckman proposed a sample selection model based on a bivariate normal distribution for dealing with this problem. Due to the non-robustness of the normal distribution, many alternatives have been introduced in the literature by assuming extensions of the normal distribution like the Student-t and skew-normal models. One common limitation of the existent sample selection models is that they require a transformation of the outcome of interest, which is common R+-valued, such as income and wage. With this, data are analyzed on a non-original scale which complicates the interpretation of the parameters. In this paper, we propose a sample selection model based on the bivariate Birnbaum–Saunders distribution, which has the same number of parameters that the classical Heckman model. Further, our associated outcome equation is R+-valued. We discuss estimation by maximum likelihood and present some Monte Carlo simulation studies. An empirical application to the ambulatory expenditures data from the 2001 Medical Expenditure Panel Survey is presented.
Investigation of agricultural biomass residues in liquefaction process , B. S. Leite, D. J. O. Ferreira, S. A. F. Leite, F. S. Bastos, V. F. C. Lins, B. T. Castro International Journal of Environmental Science and Development, 2019 The use of agricultural biomass residues as an alternative of fossil derivatives have been extensively investigated in the last years due to environmental concerns. In this context, the liquefaction appears as an alternative to use these renewable sources to produce green materials. The present work aims to synthesize polyols from the cassava peels (CP), lemon bagasse (LB) and rice husk (RH) in order to obtain biopolyols suitable to produce polyurethane foams and add value to these residues. Besides the production of the green foams, this work has also the objective to evaluate how composition of the biomass (e.g.: solid, lignin and holocellulose content) can be related to the process yield and the characteristics of the biopolyol (e.g: hydroxyl number). The polyols were synthesized from the biomass liquefaction, using crude glycerol as solvent (a by-product of biodiesel industry) and sulfuric acid as catalyst. The liquefaction was performed using an autoclave, operated at 125 C and 1.84 atm. Liquefaction yield varied from 38 to 91 %, according to biomass and process parameters used. It was observed that CP, which has the higher volatile solids content and the lower lignin plus holocellulose content, had the higher liquefaction yield. Polyol's hydroxyl number from RH had the lowest values and lower variation, according to process parameters. Liquefaction yield and hydroxyl number from LB presented great response to process parameters used.
