Permutation Strategies for Inference in ANOVA-Based Models for Nonorthogonal Designs Including Continuous Covariates
Research output: Contribution to journal › Journal article › Research › peer-review
Analysis of variance and linear models is undoubtedly one of the most useful statistical contributions to experimental and observational science. With the ability to characterize a system through multivariate responses, these methods have emerged to be general tools regardless of response dimensionality. Contemporary methods for establishing statistical inference, such as ANOVA simultaneous component analysis (ASCA), are based on Monte Carlo sampling; however, a flat uniform resampling scheme may violate the structure of the uncertainty for unbalanced designs as well as for observational data. In this work, we provide permutation strategies for inferential testing for unbalanced designs including interaction models and establish nonuniform randomization based on the concept of propensity score matching. Lastly, we provide a general method for modelling continuous covariates based on kernel smoothers. All methods are characterized on their ability to provide unbiased Type I error results.
Original language | English |
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Journal | Journal of Chemometrics |
ISSN | 0886-9383 |
DOIs | |
Publication status | E-pub ahead of print - 2024 |
Bibliographical note
Publisher Copyright:
© 2024 The Author(s). Journal of Chemometrics published by John Wiley & Sons Ltd.
- ANOVA, confounding, continuous covariates, random permutation
Research areas
ID: 399668447