scholarly journals Smoothing in Ordinal Regression: An Application to Sensory Data

Stats ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 616-633
Author(s):  
Ejike R. Ugba ◽  
Daniel Mörlein ◽  
Jan Gertheiss
Keyword(s):  
Simulation Study ◽  
Ordinal Regression ◽  
Boar Taint ◽  
Proportional Odds ◽  
Individual Level ◽  
Sensory Data ◽  
Flexible Modeling ◽  
Smooth Effects ◽  
Proportional Odds Assumption ◽  
Proportional Odds Models

The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects of predictors (androstenone and skatole) vary between response categories. For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories. The usefulness of SERP is further demonstrated through a simulation study. Besides flexible and accurate modeling, SERP also enables fitting of parameters in cases where the pure, unpenalized non-proportional odds model fails to converge.

Constrained and Unconstrained Partial Adjacent Category Logit Models for Ordinal Response Variables

2015 ◽  
Vol 47 (2) ◽  
pp. 169-206 ◽  
Author(s):  
Andrew S. Fullerton ◽  
Jun Xu
Keyword(s):  
General Social Survey ◽  
Estimation Method ◽  
Logit Models ◽  
Proportional Odds ◽  
Social Survey ◽  
Partial Model ◽  
Ordinal Response ◽  
Proportional Odds Assumption ◽  
Using Data ◽  
Response Variables

Adjacent category logit models are ordered regression models that focus on comparisons of adjacent categories. These models are particularly useful for ordinal response variables with categories that are of substantive interest. In this article, we consider unconstrained and constrained versions of the partial adjacent category logit model, which is an extension of the traditional model that relaxes the proportional odds assumption for a subset of independent variables. In the unconstrained partial model, the variables without proportional odds have coefficients that freely vary across cutpoint equations, whereas in the constrained partial model two or more of these variables have coefficients that vary by common factors. We improve upon an earlier formulation of the constrained partial adjacent category model by introducing a new estimation method and conceptual justification for the model. Additionally, we discuss the connections between partial adjacent category models and other models within the adjacent approach, including stereotype logit and multinomial logit. We show that the constrained and unconstrained partial models differ only in terms of the number of dimensions required to describe the effects of variables with nonproportional odds. Finally, we illustrate the partial adjacent category logit models with empirical examples using data from the international social survey program and the general social survey.

A Two-stage Multilevel Randomized Response Technique With Proportional Odds Models and Missing Covariates

2020 ◽  
pp. 004912412091495
Author(s):  
Shu-Hui Hsieh ◽  
Shen-Ming Lee ◽  
Chin-Shang Li
Keyword(s):  
Missing At Random ◽  
Missing Covariates ◽  
Randomized Response ◽  
Personal Privacy ◽  
Proportional Odds ◽  
Two Stage ◽  
Inverse Probability ◽  
Wide Range ◽  
Proportional Odds Models ◽  
Income Data

Surveys of income are complicated by the sensitive nature of the topic. The problem researchers face is how to encourage participants to respond and to provide truthful responses in surveys. To correct biases induced by nonresponse or underreporting, we propose a two-stage multilevel randomized response (MRR) technique to investigate the true level of income and to protect personal privacy. For a wide range of applications, we present a proportional odds model for two-stage MRR data and apply inverse probability weighting and multiple imputation methods to deal with covariates on some subjects that are missing at random. A simulation study is conducted to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. The practicality of the proposed methods is illustrated with the regular monthly income data collected in the Taiwan Social Change Survey. Furthermore, we provide an estimate of personal regular monthly mean income.

scholarly journals Predicting preference-based utility values using partial proportional odds models

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Roberta Ara ◽  
Ben Kearns ◽  
Ben A vanHout ◽  
John E Brazier
Keyword(s):  
Proportional Odds ◽  
Utility Values ◽  
Proportional Odds Models

scholarly journals Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference

2016 ◽  
Vol 12 (1) ◽  
pp. 97-115 ◽  
Author(s):  
Mireille E. Schnitzer ◽  
Judith J. Lok ◽  
Susan Gruber
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Causal Inference ◽  
Simulation Study ◽  
Learning Approaches ◽  
Minimum Loss ◽  
Knowledge Based ◽  
Flexible Modeling ◽  
Selection For ◽  
Highly Correlated

Abstract This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.

Testing the proportional odds assumption in multiply imputed ordinal longitudinal data

2015 ◽  
Vol 42 (10) ◽  
pp. 2257-2279 ◽  
Author(s):  
A.F. Donneau ◽  
M. Mauer ◽  
P. Lambert ◽  
E. Lesaffre ◽  
A. Albert
Keyword(s):  
Longitudinal Data ◽  
Proportional Odds ◽  
Proportional Odds Assumption ◽  
Multiply Imputed
Sign in / Sign up

Export Citation Format

Share Document