A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness of fit

2011 ◽  
Vol 23 (3) ◽  
pp. 763-780 ◽  
Author(s):  
Zhong Guan ◽  
Cheng Peng
Keyword(s):  
Empirical Likelihood ◽  
Goodness Of Fit ◽  
Proportional Odds Model ◽  
Proportional Odds ◽  
Likelihood Approach

scholarly journals Ordinal logistic regression models: application in quality of life studies

2008 ◽  
Vol 24 (suppl 4) ◽  
pp. s581-s591 ◽  
Author(s):  
Mery Natali Silva Abreu ◽  
Arminda Lucia Siqueira ◽  
Clareci Silva Cardoso ◽  
Waleska Teixeira Caiaffa
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Outcome Variable ◽  
Proportional Odds ◽  
Logistic Regression Models

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

scholarly journals Empirical likelihood approach to goodness of fit testing

Bernoulli ◽  
2013 ◽  
Vol 19 (3) ◽  
pp. 954-981 ◽  
Author(s):  
Hanxiang Peng ◽  
Anton Schick
Keyword(s):  
Empirical Likelihood ◽  
Goodness Of Fit ◽  
Goodness Of Fit Testing ◽  
Fit Testing ◽  
Likelihood Approach

scholarly journals A COMPARISON OF POLYTOMOUS MODEL WITH PROPORTIONAL ODDS AND NON-PROPORTIONAL ODDS MODEL ON BIRTH SIZE CASE IN INDONESIA

2021 ◽  
Vol 14 (1) ◽  
pp. 79-88
Author(s):  
Yenni Kurniawati ◽  
Anang Kurnia ◽  
Kusman Sadik
Keyword(s):  
Risk Factors ◽  
Significant Influence ◽  
Alternative Model ◽  
Goodness Of Fit ◽  
Proportional Odds Model ◽  
Birth Size ◽  
Proportional Odds ◽  
Proportional Odds Assumption ◽  
Deviance Test ◽  
And Gender

The proportional odds model (POM) and the non-proportional odds model (NPOM) are very useful in ordinal modeling. However, the proportional odds assumption is often violated in practice. In this paper, the non-proportional odds model is chosen as an alternative model when the proportional odds assumption is not violated. This paper aims to compare Proportional Odds Model (POM) and Non-Proportional Odds Model (NPOM) in cases of birth size in Indonesia based on the 2017 Indonesian Demographic and Health Survey (IDHS) data. The results showed that in the POM there was a violation of the proportional odds assumption, so the alternative NPOM model was used. NPOM had better use than POM. The goodness of fit shows that the deviance test failed to reject H0, and the value of Mac Fadden R2 is higher than POM. The risk factors that have a significant influence on all categories of birth size are the residence and gender of the child.

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