TY - GEN

T1 - Estimation of Nonparametric Ordinal Logistic Regression Model using Generalized Additive Models (GAM) Method Based on Local Scoring Algorithm

AU - Rifada, Marisa

AU - Chamidah, Nur

AU - Ningrum, Ratih Ardiati

N1 - Funding Information:
The authors thanks to Universitas Airlangga for financial support of this research through The Excellence Research 2021 with Rector's decree number: 212/UN3/2021.
Publisher Copyright:
© 2022 American Institute of Physics Inc.. All rights reserved.

PY - 2022/10/11

Y1 - 2022/10/11

N2 - One of the statistical methods used to describe the relationship between categorical scale response variable and categorical or continuous variable of predictors is logistic regression analysis. If the response variable has ordinal scale, it is called ordinal logistic regression. The ordinal response variables is common used in scientific research. There are two approaches to the regression model, i.e. parametric and nonparametric approach. We develop the nonparametric ordinal logistic regression which is an expansion of the ordinal logistic regression model where the regression function is estimated using a nonparametric approach. The aim of this study is determine the regression function estimators of the nonparametric ordinal logistic regression model using Generalized Additive Models (GAM) method based on local scoring algorithm. The GAM method assumes that the regression function is expressed as the sum of the regression functions of each component predictor variables. The local scoring algorithm consists of two loops, namely the scoring step (outer loop) which is iterated until the deviance value converges and the backfitting step (inner loop) is iterated until the Residual Sum of Squares (RSS) value converges.

AB - One of the statistical methods used to describe the relationship between categorical scale response variable and categorical or continuous variable of predictors is logistic regression analysis. If the response variable has ordinal scale, it is called ordinal logistic regression. The ordinal response variables is common used in scientific research. There are two approaches to the regression model, i.e. parametric and nonparametric approach. We develop the nonparametric ordinal logistic regression which is an expansion of the ordinal logistic regression model where the regression function is estimated using a nonparametric approach. The aim of this study is determine the regression function estimators of the nonparametric ordinal logistic regression model using Generalized Additive Models (GAM) method based on local scoring algorithm. The GAM method assumes that the regression function is expressed as the sum of the regression functions of each component predictor variables. The local scoring algorithm consists of two loops, namely the scoring step (outer loop) which is iterated until the deviance value converges and the backfitting step (inner loop) is iterated until the Residual Sum of Squares (RSS) value converges.

UR - http://www.scopus.com/inward/record.url?scp=85140221861&partnerID=8YFLogxK

U2 - 10.1063/5.0111771

DO - 10.1063/5.0111771

M3 - Conference contribution

AN - SCOPUS:85140221861

T3 - AIP Conference Proceedings

BT - 3rd International Conference on Mathematics and Sciences, ICMSc 2021

A2 - Nugroho, Rudy Agung

A2 - Allo, Veliyana Londong

A2 - Siringoringo, Meiliyani

A2 - Prangga, Surya

A2 - Wahidah, null

A2 - Munir, Rahmiati

A2 - Hiyahara, Irfan Ashari

PB - American Institute of Physics Inc.

T2 - 3rd International Conference on Mathematics and Sciences 2021: A Brighter Future with Tropical Innovation in the Application of Industry 4.0, ICMSc 2021

Y2 - 12 October 2021 through 13 October 2021

ER -