Published date: Mar 27 2024

Journal Title: KnE Life Sciences

Issue title: International Conference On Mathematics And Science Education (ICMScE 2022): Life Sciences

Pages: 177–185

DOI: 10.18502/kls.v8i1.15546

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

Purhadi .purhadi@statistika.its.ac.idDepartment of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia

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

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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