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A new regression model for bimodal data and applications in agriculture

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

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.

Suggested Citation

  • Julio Cezar Souza Vasconcelos & Gauss Moutinho Cordeiro & Edwin Moises Marcos Ortega & Édila Maria de Rezende, 2021. "A new regression model for bimodal data and applications in agriculture," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(2), pages 349-372, January.
    • Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:349-372
    DOI: 10.1080/02664763.2020.1723503
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    Cited by:

    1. Oksana Mandrikova & Nadezhda Fetisova & Yuriy Polozov, 2021. "Hybrid Model for Time Series of Complex Structure with ARIMA Components," Mathematics, MDPI, vol. 9(10), pages 1-18, May.

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