Title: Sequential estimation for the multiple linear regression models with balanced loss functions

Author (s):: Sengupta R.N.; Bapat S.R.; Joshi N.

Journal: Sequential Analysis

Month and Year: May 2024

Abstract: Sequential analysis (SA) as a sampling technique has notable advantages like smaller average sample size and reduced value of risk compared to similarly comparable fixed-sample techniques. In this study, we first propose a few models for the estimation of the regression parameters or functions of parameters under the multiple linear regression (MLR) setup using a balanced loss function (BLF). Thereafter, we obtain the expressions of risk functions and optimal fixed sample sizes for the proposed models based on the bounded risk criteria. We establish that no fixed-sample procedures can tackle these estimation problems. Hence, we propose different multistage sampling methodologies, viz. (i) two-stage sampling, (ii) three-stage sampling, (iii) purely sequential sampling, and (iv) batch sequential sampling, and corroborate the same with the detailed simulation and real data analyses. © 2024 Taylor & Francis Group, LLC.

Document Type: Article

DOI: https://doi.org/10.1080/07474946.2024.2329145