Literature DB >> 25813464

Advances in Modeling Model Discrepancy: Comment on Wu and Browne (2015).

Robert C MacCallum1, Anthony O'Hagan.   

Abstract

Wu and Browne (Psychometrika, 79, 2015) have proposed an innovative approach to modeling discrepancy between a covariance structure model and the population that the model is intended to represent. Their contribution is related to ongoing developments in the field of Uncertainty Quantification (UQ) on modeling and quantifying effects of model discrepancy. We provide an overview of basic principles of UQ and some relevant developments and we examine the Wu-Browne work in that context. We view the Wu-Browne contribution as a seminal development providing a foundation for further work on the critical problem of model discrepancy in statistical modeling in psychological research.

Mesh:

Year:  2015        PMID: 25813464     DOI: 10.1007/s11336-015-9452-2

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  5 in total

1.  Model selection in covariance structures analysis and the "problem" of sample size: a clarification.

Authors:  R Cudeck; S J Henly
Journal:  Psychol Bull       Date:  1991-05       Impact factor: 17.737

2.  2001 Presidential Address: Working with Imperfect Models.

Authors:  Robert C MacCallum
Journal:  Multivariate Behav Res       Date:  2003-01-01       Impact factor: 5.923

3.  Recovery of Weak Common Factors by Maximum Likelihood and Ordinary Least Squares Estimation.

Authors:  Nancy E Briggs; Robert C MacCallum
Journal:  Multivariate Behav Res       Date:  2003-01-01       Impact factor: 5.923

4.  Sample Size in Factor Analysis: The Role of Model Error.

Authors:  R C MacCallum; K F Widaman; K J Preacher; S Hong
Journal:  Multivariate Behav Res       Date:  2001-10-01       Impact factor: 5.923

5.  Quantifying Adventitious Error in a Covariance Structure as a Random Effect.

Authors:  Hao Wu; Michael W Browne
Journal:  Psychometrika       Date:  2015-03-27       Impact factor: 2.500

  5 in total
  1 in total

Review 1.  Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting.

Authors:  Peyman Abbaszadeh; David F Muñoz; Hamed Moftakhari; Keighobad Jafarzadegan; Hamid Moradkhani
Journal:  iScience       Date:  2022-09-23
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.