Bounded influence function based inference in joint modelling of ordinal partial linear model and accelerated failure time model.
A common objective in longitudinal studies is to characterize the relationship between a longitudinal response process and a time-to-event data. Ordinal nature of the response and possible missing information on covariates add complications to the joint model. In such circumstances, some influential observations often present in the data may upset the analysis. In this paper, a joint model based on ordinal partial mixed model and an accelerated failure time model is used, to account for the repeated ordered response and time-to-event data, respectively. Here, we propose an influence function-based robust estimation method. Monte Carlo expectation maximization method-based algorithm is used for parameter estimation. A detailed simulation study has been done to evaluate the performance of the proposed method. As an application, a data on muscular dystrophy among children is used. Robust estimates are then compared with classical maximum likelihood estimates.
Department of Statistics, Visva-Bharati University, West Bengal, India arindom.chakraborty@visva-bharati.ac.in.
Arindom
A
Chakraborty
D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D009010:Monte Carlo Method; D009136:Muscular Dystrophies / Q000175:diagnosis / Q000235:genetics / Q000503:physiopathology; D011379:Prognosis; D055815:Young Adult
MCMHNR algorithm; longitudinal; mixed effects model; muscular dystrophy syndrome; ordinal data