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Budinirmala, K., ., P., & Choiruddin, A. (2024). Parameter Estimation and Hypothesis Testing on Bivariate Log-Normal Regression Models. KnE Life Sciences, 8(1), 177–185. https://doi.org/10.18502/kls.v8i1.15546

https://doi.org/10.18502/kls.v8i1.15546

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authors

  • Kadek Budinirmala

    Kadek Budinirmala

    Department of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia
  • Purhadi .

    Purhadi .

    Department of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia
  • Achmad Choiruddin

    Achmad Choiruddin

    Department of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia

Published Date

  • Mar 27, 2024

Parameter Estimation and Hypothesis Testing on Bivariate Log-Normal Regression Models

KnE Life Sciences / International Conference On Mathematics And Science Education (ICMScE 2022): Life Sciences / Pages 177–185

Abstract

This study aims to introduce a bivariate Log-Normal regression model and to develop a technique for parameter estimation and hypothesis testing. We term the model Bivariate Log-Normal Regression (BLNR). The estimation procedure is conducted by the standard Maximum Likelihood Estimation (MLE) employing the Newton-Raphson method. To perform hypothesis testing, we adapt the Maximum Likelihood Ratio Test (MLRT) for simultaneous testing with test statistics which, for large n, follows Chi-Square distribution with degrees of freedom p. In addition, the partial testing is derived from a central limit theorem which results in a Z-test statistic.


Keywords: parameter estimation, hypothesis testing, bivariate log, normal regression

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