A new regression model for bimodal data and applications in agriculture

Julio Cezar Souza Vasconcelos, Gauss Moutinho Cordeiro, Edwin Moises Marcos Ortega and Édila Maria de Rezende

Journal of Applied Statistics, 2021, vol. 48, issue 2, 349-372

Abstract: We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.

Date: 2021
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DOI: 10.1080/02664763.2020.1723503

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