This paper considers the modeling of zero-inflated circular measurements concerning real case studies from medical sciences. Circular-circular regression models have been discussed in the statistical literature and illustrated with various real-life applications. However, there are no models to deal with zero-inflated response as well as a covariate simultaneously. The Möbius transformation based two-stage circular-circular regression model is proposed, and the Bayesian estimation of the model parameters is suggested using the MCMC algorithm. Simulation results show the superiority of the performance of the proposed method over the existing competitors. The method is applied to analyse real datasets on astigmatism due to cataract surgery and abnormal gait related to orthopaedic impairment. The methodology proposed can assist in efficient decision making during treatment or postoperative care.
The work of Dr. Prajamitra Bhuyan was supported in part by the Lloyd’s Register Foundation programme on data-centric engineering at the Alan Turing Institute, UK.
The authors are thankful to Prof. Anup Dewanji, Dr. Arnab Chakraborty, Prof. Debasis Sengupta, Dr. Jayabrata Biswas, Dr. Sourabh Bhattacharya, and Mr. Sudipta Kundu for many helpful comments and suggestions.
"Two-stage circular-circular regression with zero inflation: Application to medical sciences." Ann. Appl. Stat. 15 (3) 1343 - 1365, September 2021. https://doi.org/10.1214/20-AOAS1429