.svg){kind=logo}
A BAYESIAN APPROACH FOR ESTIMATION OF PARAMETERS AND MISSING VALUES IN FACTORIAL EXPERIMENTAL DESIGNS
Keywords:
factorial experimental design, missing values, Bayesian approach, parameter estimation.DOI:
https://doi.org/10.17654/0972086323010Abstract
Factorial experimental designs have been widely used in many industrial areas. This paper presents a Bayesian approach for missing data estimation for the analysis of a factorial experiment. The proposed methodology could be alternative to commonly used statistical approaches in that it features both the easy implementation and the learning capability of Bayesian approach.
Received: February 23, 2023
Accepted: April 22, 2023
References
R. Ajantha and N. Ch. Bhatra Charyulu, Bayesian approach for the estimation of missing responses in randomized block design, Thail. Stat. 20(1) (2022), 227-232.
N. Draper and D. Stoneman, Estimating missing values in un-replicated two-level factorial and fractional factorial designs, Biometrics 20 (1964), 443-458.
C. H. Goulden, Methods of Statistical Analysis, 2nd ed., Wiley, New York, Vol. 318, 1952.
W. G. Cochran and G. M. Cox, Experimental Designs, Wiley, New York, Vol. 202, 1950.
T. W. Anderson, Maximum likelihood estimates for the multivariate normal distribution when some observations are missing, J. Amer. Statist. Assoc. 52 (1957), 200-203.
C. K. Enders, Applied Missing Data Analysis, Guilford Press, NY, 2010.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA
This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pusha Publishing House as the original publisher, including title and author(s) if applicable.
Non-Commercial Use: For non-commercial purposes only. No commercial activities without explicit permission.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pusha Publishing House for more info or permissions.
