Learning a bayesian network from ordinal data

Bayesian networks are graphical models that represent the joint distributionof a set of variables using directed acyclic graphs. When the dependence structure is unknown (or partially known) the network can be learnt from data. In this paper, we propose a constraint-based method to perform Bayesian networks structural learning in presence of ordinal variables. The new procedure, called OPC, represents a variation of the PC algorithm. A nonparametric test, appropriate for ordinal variables, has been used. It will be shown that, in some situation, the OPC algorithm is a solution more efficient than the PC algorithm.Structural Learning, Monotone Association, Nonparametric Methods

Ordinal and Cardinal Measures of Health Inequality - An Empirical Comparison

When measuring health inequality using ordinal data, analysts typically must choose between indices specifically based upon ordinal data and more standard indices using ordinal data which has been transformed into cardinal data. This paper compares inequality rankings across a number of different approaches and finds considerable sensitivity to the choice between ordinal and cardinal based indices. There is relatively little sensitivity to the ethical choices made by the analyst in terms of the weight attached to different parts of the distribution.Inequality, cardinal, ordinal

Semiparametric Modeling of Ordinal Data

Parametric models for categorical ordinal response variables, like the proportional odds model or the continuation ratio model, assume that the predictor is given as a linear form of covariates. In this paper the parametric models are extended to a semiparametric or partially parametric form where parts of the covariates are modeled linearly and parts are modeled as unspecified but smooth functions. Estimation is based on a combination of local likelihood and profile likelihood and asymptotic properties of the estimates are derived. In a simulation study it is demonstrated that the profile likelihood approach is to be preferred over a backfitting procedure. A data example shows the applicability of the models

Using rank data to estimate health state utility models

In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using ordinal data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that ordinal data have the potential to provide useful insights into community health state preferences. However, important questions remain