شماره ركورد كنفرانس
4109
عنوان مقاله
A sensitivity analysis for dropout mechanism in longitudinal data using reversible jump MCMC
پديدآورندگان
Baghfalaki T Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran , Jalali Farahani E Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
تعداد صفحه
11
كليدواژه
Bayesian approach , Longitudinal data , Marginal model , Missingness mechanism , Reversible Jump MCMC
سال انتشار
1396
عنوان كنفرانس
يازدهمين سمينار ملي احتمال و فرآيندهاي تصادفي
زبان مدرك
انگليسي
چكيده فارسي
Existence of missing values is an inseparable part of longitudinal studies in epidemi-
ology, medical and clinical studies. Usually researchers, for simplicity, ignore the missingness
mechanism while, ignoring a not at random mechanism may lead to misleading results. In this
paper, we use a Bayesian paradigm for fitting selection model of Heckman (Heckman, 1976), which
allows the non-ignorable missingness for longitudinal data. Also, We use reversible-jump Markov
chain Monte Carlo to allow the model to choose between non-ignorable and ignorable structures
for missingness mechanism, and show how the selection can be incorporated. The approach is also
used for analyzing a real data set
كشور
ايران
لينک به اين مدرک