KnE Life Sciences

ISSN: 2413-0877

The latest conference proceedings on life sciences, medicine and pharmacology.

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

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

Authors:

Kadek BudinirmalaEmail: N/A
Affiliation: Department of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia
Biography:

Purhadi .Email: purhadi@statistika.its.ac.id
Affiliation: Department of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia
Biography:

Achmad ChoiruddinEmail: N/A
Affiliation: Department of Statistics, Faculty of Science and Data Analysis, Institut Teknologi Sepuluh November, East Java, Indonesia
Biography:

Kadek Budinirmala

Purhadi . - purhadi@statistika.its.ac.id

Achmad Choiruddin

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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