6533b824fe1ef96bd128148a
RESEARCH PRODUCT
A penalized approach for the bivariate ordered logistic model with applications to social and medical data
Marco EneaGianfranco Lovisonsubject
Statistics and ProbabilityAssociation (object-oriented programming)05 social sciencesDale modelBivariate analysisLogistic regression01 natural sciencesbivariate ordered logistic modelSet (abstract data type)010104 statistics & probabilityordinal associationpenalized maximum likelihood estimation0502 economics and businessStatisticsCovariateDale model bivariate ordered logistic model penalized maximum likelihood estimation ordinal associationSettore SECS-S/05 - Statistica Sociale0101 mathematicsStatistics Probability and UncertaintyMarginal distributionSettore SECS-S/01 - Statistica050205 econometrics MathematicsOrdinal associationdescription
Bivariate ordered logistic models (BOLMs) are appealing to jointly model the marginal distribution of two ordered responses and their association, given a set of covariates. When the number of categories of the responses increases, the number of global odds ratios to be estimated also increases, and estimation gets problematic. In this work we propose a non-parametric approach for the maximum likelihood (ML) estimation of a BOLM, wherein penalties to the differences between adjacent row and column effects are applied. Our proposal is then compared to the Goodman and Dale models. Some simulation results as well as analyses of two real data sets are presented and discussed.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-18 |