TY - GEN
T1 - Parameter Estimation of The Bivariate Polynomial Ordinal Logistic Regression Model
AU - Rifada, Marisa
AU - Ratnasari, Vita
AU - Purhadi,
N1 - Publisher Copyright:
© 2022 American Institute of Physics Inc.. All rights reserved.
PY - 2022/1/25
Y1 - 2022/1/25
N2 - Ordinal logistic regression is a statistical method used to analyze the ordinal response variable with three or more categories and predictor variables that are categorical or continuous. The use of ordinal response variables is common in scientific research. We develop an extension of the bivariate ordinal logistic regression model with two correlated response variables in which the relationship between the continuous predictor variable and its logit is modeled as a polynomial form, so it is called the Bivariate Polynomial Ordinal Logistic Regression (BPOLR) model. The aims of this study are determine parameter estimators of the BPOLR model using the Maximum Likelihood Estimation (MLE) method and obtain algorithms of estimating parameters of the BPOLR model. Based on the first partial derivatives, the results are not closed-form. Therefore, it is needed a numerical optimization to obtain the maximum likelihood estimator, namely the Berndt-Hall-Hall-Hausman (BHHH) method.
AB - Ordinal logistic regression is a statistical method used to analyze the ordinal response variable with three or more categories and predictor variables that are categorical or continuous. The use of ordinal response variables is common in scientific research. We develop an extension of the bivariate ordinal logistic regression model with two correlated response variables in which the relationship between the continuous predictor variable and its logit is modeled as a polynomial form, so it is called the Bivariate Polynomial Ordinal Logistic Regression (BPOLR) model. The aims of this study are determine parameter estimators of the BPOLR model using the Maximum Likelihood Estimation (MLE) method and obtain algorithms of estimating parameters of the BPOLR model. Based on the first partial derivatives, the results are not closed-form. Therefore, it is needed a numerical optimization to obtain the maximum likelihood estimator, namely the Berndt-Hall-Hall-Hausman (BHHH) method.
UR - http://www.scopus.com/inward/record.url?scp=85147302164&partnerID=8YFLogxK
U2 - 10.1063/5.0105115
DO - 10.1063/5.0105115
M3 - Conference contribution
AN - SCOPUS:85147302164
T3 - AIP Conference Proceedings
BT - 8th International Conference and Workshop on Basic and Applied Science, ICOWOBAS 2021
A2 - Wibowo, Anjar Tri
A2 - Mardianto, M. Fariz Fadillah
A2 - Rulaningtyas, Riries
A2 - Sakti, Satya Candra Wibawa
A2 - Imron, Muhammad Fauzul
A2 - Ramadhan, Rico
PB - American Institute of Physics Inc.
T2 - 8th International Conference and Workshop on Basic and Applied Science, ICOWOBAS 2021
Y2 - 25 August 2021 through 26 August 2021
ER -