Centile estimation for a proportion response variable

Marco Enea, Mikis Stasinopoulos, Abu Hossain, Robert Rigby

Risultato della ricerca: Article

8 Citazioni (Scopus)

Abstract

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.
Lingua originaleEnglish
pagine (da-a)895-904
Numero di pagine10
RivistaStatistics in Medicine
Volume35
Stato di pubblicazionePublished - 2016

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Fingerprint Entra nei temi di ricerca di 'Centile estimation for a proportion response variable'. Insieme formano una fingerprint unica.

  • Cita questo

    Enea, M., Stasinopoulos, M., Hossain, A., & Rigby, R. (2016). Centile estimation for a proportion response variable. Statistics in Medicine, 35, 895-904.