The effects of model misspecification on shelf-life prediction of nano-sols under pH acceleration. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Tseng, Sheng-Tsaing; Yao, Yu-Cheng; Hill Wong, David Shan
• Is Part Of: Quality technology & quantitative management. Volume 17:Issue 4(2020)
• Page Start: 383
• Page End: 398
• Publication Date: 2020-07-03
• Subjects: Mis-specification analysis -- shelf-life prediction for nano-sol -- pH acceleration model -- mixture-normal distribution -- nonparametric model; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2019.1630907
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: Shelf-life prediction of liquid-type (such as nano-sol) products is an interesting research topic. Recently, a pH acceleration method has been proposed in the literature to address the shelf-life prediction of nano-sol. The time evolution of particle-size distribution was obtained and modelled. There are two approaches for modelling the particle-size distribution, either by using a parametric approach (mixture-normal distribution) or a distribution-free approach. The main goal of this study is to quantify the seriousness of model misspecification on the shelf-life prediction when the true particle-size distribution follows a mixture-normal distribution, but wrongly treated it by a distribution-free model. The results demonstrated that the relative bias of shelf-life prediction may be under-estimated up to 13.66%, while its relative variability may be seriously inflated up to 13.35 times by using a distribution-free approach. The study shows that these effects of model misspecification are moderately large and cannot be neglected even when the sample size and the measurement times are small.