-
Moment-based Estimation of Mixtures of Regression Models
Finite mixtures of regression models provide a flexible modeling framewo...
read it
-
Logistic Box-Cox Regression to Assess the Shape and Median Effect under Uncertainty about Model Specification
The shape of the relationship between a continuous exposure variable and...
read it
-
A constrained regression model for an ordinal response with ordinal predictors
A regression model is proposed for the analysis of an ordinal response v...
read it
-
Ordinal Probit Functional Regression Models with Application to Computer-Use Behavior in Rhesus Monkeys
Research in functional regression has made great strides in expanding to...
read it
-
Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes
Outcomes with a natural order commonly occur in prediction tasks and oft...
read it
-
The α-k-NN regression for compositional data
Compositional data arise in many real-life applications and versatile me...
read it
-
Bayesian Wavelet-packet Historical Functional Linear Models
Historical Functional Linear Models (HFLM) quantify associations between...
read it
A Bayesian Mixture Model for Changepoint Estimation Using Ordinal Predictors
In regression models, predictor variables with inherent ordering, such as tumor staging ranging and ECOG performance status, are commonly seen in medical settings. Statistically, it may be difficult to determine the functional form of an ordinal predictor variable. Often, such a variable is dichotomized based on whether it is above or below a certain cutoff. Other methods conveniently treat the ordinal predictor as a continuous variable and assume a linear relationship with the outcome. However, arbitrarily choosing a method may lead to inaccurate inference and treatment. In this paper, we propose a Bayesian mixture model to simultaneously assess the appropriate form of the predictor in regression models by considering the presence of a changepoint through the lens of a threshold detection problem. By using a mixture model framework to consider both dichotomous and linear forms for the variable, the estimate is a weighted average of linear and binary parameterizations. This method is applicable to continuous, binary, and survival outcomes, and easily amenable to penalized regression. We evaluated the proposed method using simulation studies and apply it to two real datasets. We provide JAGS code for easy implementation.
READ FULL TEXT
Comments
There are no comments yet.