Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.

Inferences for joint modelling of repeated ordinal scores and time to event data

Abstract: In clinical trials and other follow-up studies, it is natural that a response variable is repeatedly measured during follow-up and the occurrence of some key event is also monitored. There has been a considerable study on the joint modelling these measures together with information on covariates. But most of the studies are related to continuous outcomes. In many situations instead of observing continuous outcomes, repeated ordinal outcomes are recorded over time. The joint modelling of such serial outcomes and the time to event data then becomes a bit complicated. In this article we have attempted to analyse such models through a latent variable model. In view of the longitudinal variation on the ordinal outcome measure, it is desirable to account for the dependence between ordered categorical responses and survival time for different causes due to unobserved factors. A flexible Monte Carlo EM (MCEM) method based on exact likelihood is proposed that can simultaneously handle the longitudinal ordinal data and also the censored time to event data. A computationally more efficient MCEM method based on approximation of the likelihood is also proposed. The method is applied to a number of ordinal scores and survival data from trials of a treatment for children suffering from Duchenne Muscular Dystrophy. Finally, a simulation study is conducted to examine the finite sample properties of the proposed estimators in the joint model under two different methods.

Ensemble of decision trees with global constraints for ordinal classification

Abstract: While ordinal classification problems are common in many situations, induction of ordinal decision trees has not evolved significantly. Conventional trees for regression settings or nominal classification are commonly induced for ordinal classification problems. On the other hand a decision tree consistent with the ordinal setting is often desirable to aid decision making in such situations as credit rating. In this work we extend a recently proposed strategy based on constraints defined globally over the feature space. We propose a bootstrap technique to improve the accuracy of the baseline solution. Experiments in synthetic and real data show the benefits of our proposal.

Generating Correlated Ordinal Random Values

Abstract: Ordinal variables appear in many field of statistical research. Since working with simulated data is an accepted technique to improve models or test results there is a need for providing correlated ordinal random values with certain properties like marginal distribution and correlation structure. The present paper describes two methods for generating such values: binary conversion and a mean mapping approach. The algorithms of the two methods are described and some examples of the outcomes are shown.

A robust goodness‐of‐fit test statistic with application to ordinal regression models

Abstract: We propose a goodness-of-fit test statistic for linear regression with heterogeneous variance, which is asymptotically chi-square if the given model is correct. The test statistic is computed as a quadratic form of observed minus predicted responses. We apply the method to a linear regression for an ordinal categorical response, the wheezing status of a child (no wheeze, wheeze with cold, wheeze apart from cold) as a function of maternal smoking and city of residence.

Combining insights from quantile and ordinal regression: child malnutrition in Guatemala.

Abstract: Chronic child undernutrition is a persistent problem in developing countries and has been the focus of hundreds of studies where the primary intent is to improve targeting of public health and economic development policies. In national level cross-sectional studies undernutrition is measured as child stunting and the goal is to assess differences in prevalence among population subgroups. Several types of regression modeling frameworks have been used to study childhood stunting but the literature provides little guidance in terms of statistical properties and the ease with which the results can be communicated to the policy community. We compare the results from quantile regression and ordinal regression models. The two frameworks can be linked analytically and together yield complementary insights. We find that reflecting on interpretations from both models leads to a more thorough analysis and forces the analyst to consider the policy utility of the findings. Guatemala is used as the country focus for the study.