| Full text | |
| Author(s): |
Prataviera, Fabio
[1]
;
Silva, Antonio M. M.
[2]
;
Cardoso, Elke J. B. N.
[2]
;
Cordeiro, Gauss M.
[3]
;
Ortega, Edwin M. M.
[1]
Total Authors: 5
|
| Affiliation: | [1] Univ Sao Paulo, Dept Exact Sci, Piracicaba, SP - Brazil
[2] Univ Sao Paulo, Dept Soil Sci, Piracicaba, SP - Brazil
[3] Univ Fed Pernambuco, Dept Stat, Recife, PE - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | Applied Mathematical Modelling; v. 93, p. 148-164, MAY 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
The presence of bimodality, heteroskedasticity, zero-inflation and nonlinear effects in co variables is common in several real data applications. In this context, new regressions are proposed for data with all these characteristics. The estimation of parameters follows the maximum likelihood method. For different fixed parameters, sample sizes and percentages of zeros, various simulations are performed to assess the behavior of the estimators. Quantile residuals are defined to evaluate the assumptions of the proposed regression. Its usefulness is illustrated by an experiment conducted to assess the soil microbiology in a sugarcane field. (c) 2020 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 16/18944-3 - Climatic changes and energetic efficiency in agriculture: focusing on hydric stress, organic management and soil biology |
| Grantee: | Elke Jurandy Bran Nogueira Cardoso |
| Support Opportunities: | Research Program on Global Climate Change - Thematic Grants |