Parametric and partially linear regressions for agricultural economy data

References

• Atkinson, A. C. 1985. Plots, transformations and regression; An introduction to graphical methods of diagnostic regression analysis. Oxford: Clarendon Press Oxford.
   Google Scholar
• Bazrafkan, M., K. Zare, M. Maleki, and Z. Khodadi. 2021. Partially linear models based on heavy-tailed and asymmetrical distributions. Stochastic Environmental Research and Risk Assessment :1–11. doi:https://doi.org/10.1007/s00477-021-02101-1.
   Web of Science ®Google Scholar
• Birnbaum, Z. W, and S. C. Saunders. 1969. A new family of life distributions. Journal of Applied Probability 6 (2):319–27. doi:10.2307/3212003.
   Web of Science ®Google Scholar
• Cancho, V. G., G. D. C. Barriga, G. M. Cordeiro, E. M. M. Ortega, and A. K. Suzuki. 2021. Bayesian survival model induced by frailty for lifetime with long-term survivors. Statistica Neerlandica 75 (3):299–323. doi:10.1111/stan.12236.
   Web of Science ®Google Scholar
• Cepea. Centro de Estudos Avançados em Economia Aplicada. Cepea/Esalq/USP. Hortifruti Cepea. Disponível em: https://www.hfbrasil.org.br/br/banco-de-dados-precos-medios-dos-hortifruticolas.aspx
   Google Scholar
• Cordeiro, G. M, and A. J. Lemonte. 2011. The β-Birnbaum-Saunders distribution: An improved distribution for fatigue life modeling. Computational Statistics & Data Analysis 55 (3):1445–61. doi:10.1016/j.csda.2010.10.007.
   Web of Science ®Google Scholar
• Cordeiro, G. M., M. Alizadeh, G. Ozel, B. Hosseini, E. M. M. Ortega, and E. Altun. 2017. The generalized odd log-logistic family of distributions: Properties, regression models and applications. Journal of Statistical Computation and Simulation 87 (5):908–32. doi:10.1080/00949655.2016.1238088.
   Web of Science ®Google Scholar
• Cruz, J. N. D., E. M. M. Ortega, and G. M. Cordeiro. 2016. The log-odd log-logistic Weibull regression model: Modelling, estimation, influence diagnostics and residual analysis. Journal of Statistical Computation and Simulation 86 (8):1516–38. doi:10.1080/00949655.2015.1071376.
   Web of Science ®Google Scholar
• de Almeida, G. V. B, and J. R. F. de Lima. 2019. A manga comercializada no CEAGESP de São Paulo: análise de volume e preços. In Embrapa Semiárido-Artigo em anais de congresso (ALICE). In: Cogresso Brasileiro de Fruticultura, 26 2019, Juazeiro, BA/Petrolina, PE. Fruticultura de precisão: desafios e oportunidades-anais. Petrolina: Embrapa Semiárido: UNIVASF: SBF, 2019.
   Google Scholar
• Dunn, P. K, and G. K. Smyth. 1996. Randomized quantile residuals. Journal of Computational and Graphical Statistics 5:236–44.
   Google Scholar
• Ferreira, C. S, and G. A. Paula. 2017. Estimation and diagnostic for skew-normal partially linear models. Journal of Applied Statistics 44 (16):3033–53. doi:10.1080/02664763.2016.1267124.
   Web of Science ®Google Scholar
• Ferreira, C. S., C. B. Zeller, A. M. Mimura, and J. C. Silva. 2017. Partially linear models and their applications to change point detection of chemical process data. Journal of Applied Statistics 44 (12):2125–41. doi:10.1080/02664763.2016.1247788.
   Web of Science ®Google Scholar
• Green, P. J, and B. W. Silverman. 1993. Nonparametric regression and generalized linear models: A roughness penalty approach. New York: Chapman and Hall.
   Google Scholar
• Ibacache-Púlgar, G, and G. A. Paula. 2011. Local influence for student-t partially linear models. Computational Statistics & Data Analysis 55 (3):1462–78. doi:10.1016/j.csda.2010.10.009.
   Web of Science ®Google Scholar
• Instituto de Economia Agrícola (IEA). Imóveis rurais com benfeitorias. Disponível em. http://www.iea.agricultura.sp.gov.br/out/Bancodedados.php
   Google Scholar
• Leiva, V. 2016. The Birnbaum-Saunders distribution. New York, USA: Academic Press.
   Google Scholar
• Manghi, R. F., F. J. A. Cysneiros, and G. A. Paula. 2019. Generalized additive partial linear models for analyzing correlated data. Computational Statistics & Data Analysis 129:47–60. doi:10.1016/j.csda.2018.08.005.
   Web of Science ®Google Scholar
• Ortega, E. M. M., J. N. da Cruz, and G. M. Cordeiro. 2019. The log-odd logistic-Weibull regression model under informative censoring. Model Assisted Statistics and Applications 14 (3):239–54. doi:10.3233/MAS-190464.
   Google Scholar
• Ortega, E. M. M., G. M. Cordeiro, A. K. Suzuki, and T. G. Ramires. 2017. A new extended Birnbaum-Saunders model with cure fraction: Classical and Bayesian approach. Communications for Statistical Applications and Methods 24 (4):397–419. doi:10.5351/CSAM.2017.24.4.397.
   Web of Science ®Google Scholar
• Ortega, E. M. M., A. J. Lemonte, G. M. Cordeiro, and J. Nilton da Cruz. 2016. The odd Birnbaum-Saunders regression model with applications to lifetime data. Journal of Statistical Theory and Practice 10 (4):780–804. doi:10.1080/15598608.2016.1224746.
   Web of Science ®Google Scholar
• Prataviera, F., E. M. M. Ortega, G. M. Cordeiro, R. R. Pescim, and B. A. Verssani. 2018. A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems. Reliability Engineering & System Safety 176:13–26. doi:10.1016/j.ress.2018.03.034.
   Web of Science ®Google Scholar
• Prataviera, F., E. M. M. Ortega, G. M. Cordeiro, and A. D. S. Braga. 2019. The heteroscedastic odd log-logistic generalized gamma regression model for censored data. Communications in Statistics - Simulation and Computation 48 (6):1815–39. doi:10.1080/03610918.2018.1423695.
   Web of Science ®Google Scholar
• Prataviera, F., A. M. Batista, E. M. M. Ortega, G. M. Cordeiro, and B. M. Silva. 2021a. The Logit exponentiated power exponential regression with applications. Annals of Data Science 5:1–19. doi:10.1007/s40745-021-00347-8.
   Google Scholar
• Prataviera, F., E. M. M. Ortega, and G. M. Cordeiro. 2021. An extended Maxwell semiparametric regression for censored and uncensored data. Communications in Statistics - Simulation and Computation 50:1–22. doi:10.1080/03610918.2021.1934021.
   Google Scholar
• Relvas, C. E. M, and G. A. Paula. 2016. Partially linear models with first-order autoregressive symmetric errors. Statistical Papers 57 (3):795–825. doi:10.1007/s00362-015-0680-4.
   Web of Science ®Google Scholar
• Silva, F. C., G. A. Paula, and G. C. Lana. 2022. Estimation and diagnostic for partially linear models with first-order autoregressive skew-normal errors. Computational Statistics 37 (1):445–68. doi:10.1007/s00180-021-01130-2.
   Web of Science ®Google Scholar
• Souza, A, and J. Lima. 2022. Comportamento dos preços de manga Palmer ao produtor do Vale do Submédio São Francisco. Revista de Economia e Sociologia Rural 61:1–20.
   Google Scholar
• Souza Vasconcelos, J. C., G. M. Cordeiro, E. M. M. Ortega, and E. G. Araújo. 2019. The new odd log-logistic generalized inverse Gaussian regression model. Journal of Probability and Statistics 2019:1–13. doi:10.1155/2019/8575424.
   Web of Science ®Google Scholar
• Souza Vasconcelos, J. C, and C. Villegas. 2021. Generalized symmetrical partial linear model. Journal of Applied Statistics 48 (3):557–72. doi:10.1080/02664763.2020.1726301.
   PubMed Web of Science ®Google Scholar
• Vasconcelos, J. C. S., E. M. M. Ortega, J. S. Vasconcelos, G. M. Cordeiro, A. L. Vivan, and M. A. M. Biaggioni. 2021. A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil. Journal of Applied Statistics 61:1–18.
   Google Scholar
• Vigas, V. P., E. M. M. Ortega, G. M. Cordeiro, A. K. Suzuki, and G. O. Silva. 2021. The new Neyman type A generalized odd log-logistic-G-family with cure fraction. Journal of Applied Statistics 61:1–20.
   Google Scholar
• Stasinopoulos, D. M, and R. A. Rigby. 2005. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics) 54:507–54.
   Web of Science ®Google Scholar
• Stasinopoulos, D. M, and R. A. Rigby. 2007. Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software 23 (7):1–46. doi:10.18637/jss.v023.i07.
   Web of Science ®Google Scholar
• Stasinopoulos, M., B. Rigby, and C. Akantziliotou. 2008. Instructions on how to use the GAMLSS package in R, Second Edition. http://gamlss.com/wpcontent/uploads/2013/01/gamlss-manual.pdf
   Google Scholar
• Stasinopoulos, M. D., R. A. Rigby, G. Z. Heller, V. Voudouris, and F. De Bastiani. 2017. Flexible regression and smoothing: Using GAMLSS in R. Boca Raton, FL: Chapman and Hall/CRC The R Series.
   Google Scholar
• Terras, R. 1981. A Miller algorithm for an incomplete Bessel function. Journal of Computational Physics 39 (1):233–40. doi:10.1016/0021-9991(81)90147-9.
   Web of Science ®Google Scholar