6533b7d1fe1ef96bd125d309
RESEARCH PRODUCT
Centile estimation for a proportion response variable.
Abu HossainRobert A. RigbyDimitrios StasinopoulosMarco Eneasubject
dewey510Least-Squares AnalysiStatistics and ProbabilityMaleModels StatisticalLogistic ModelGeneralised Tobit modelEpidemiologyFractional dataLogit skew Student t distributionStatistical DistributionLogistic ModelsGAMLSSBeta inflated distributionHumansComputer SimulationSettore SECS-S/05 - Statistica SocialeLeast-Squares AnalysisSettore SECS-S/01 - StatisticaLungHumanStatistical Distributionsdescription
This paper introduces two general models for computing centiles when the response variable Y can take values between 0 and 1, inclusive of 0 or 1. The models developed are more flexible alternatives to the beta inflated distribution. The first proposed model employs a flexible four parameter logit skew Student t (logitSST) distribution to model the response variable Y on the unit interval (0, 1), excluding 0 and 1. This model is then extended to the inflated logitSST distribution for Y on the unit interval, including 1. The second model developed in this paper is a generalised Tobit model for Y on the unit interval, including 1. Applying these two models to (1-Y) rather than Y enables modelling of Y on the unit interval including 0 rather than 1. An application of the new models to real data shows that they can provide superior fits.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-01-05 | Statistics in medicine |