Parameter estimation and hypothesis testing on three parameters log normal regression

Diantini, Ni Luh Sri; Purhadi, Purhadi; Choiruddin, Achmad

doi:10.1063/5.0104443

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2024

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Cited by 5 publications

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“…This issue cannot be handle by univariate version of Log-Normal Regression (LNR). This study can be regard as an extension of univariate LNR previously developed by [10,11].…”

Section: Introductionmentioning

confidence: 99%

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

Budinirmala,

Choiruddin

2024

KLS

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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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“…This issue cannot be handle by univariate version of Log-Normal Regression (LNR). This study can be regard as an extension of univariate LNR previously developed by [10,11].…”

Section: Introductionmentioning

confidence: 99%