Multiplicative effects in two‐way analysis of variance
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Publication:5657562
DOI10.1111/j.1467-9574.1972.tb00173.xzbMath0245.62068OpenAlexW2100666437MaRDI QIDQ5657562
A. C. van Eijnsbergen, L. C. A. Corsten
Publication date: 1972
Published in: Statistica Neerlandica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.1467-9574.1972.tb00173.x
Related Items (16)
A New Method for Testing Interaction in Unreplicated Two-Way Analysis of Variance ⋮ An analysis of a multivariate two-way model with interaction and no replication ⋮ A method for testing interaction in unreplicated two-way tables: using all pairwise interaction contrasts ⋮ Unnamed Item ⋮ A Method for Testing Additivity in Unreplicated Two‐Way Layouts Based on Combining Multiple Interaction Tests ⋮ Shifted multiplicative models for nonadditive two-way tables ⋮ Inference on covariance matrices under rank restrictions ⋮ Estimation of general linear-bilinear models for two-way tables ⋮ Testing interaction in three-way anova tables with a single observation per cell ⋮ Principal component analysis with external information on both subjects and variables ⋮ Two-way ANOVA models with unbalanced data ⋮ Asymptotic distribution of the latent roots of the noncentral wishart distribution and the power of the likelihood ratio test for nonadditivity ⋮ A comparison of three invariant tests of additivity in two-way classifications with no replications ⋮ On the distributions of the ratios of the extreme roots to the trace of the Wishart matrix ⋮ Response surface model diagnosis in two-way tables ⋮ Tests for dimensionality and interactions of mean vectors under general and reducible covariance structures
Cites Work
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- A statistical model which combines features of factor analytic and analysis of variance techniques
- Non-Additivity in Two-Way Analysis of Variance
- Percentage points of the extreme roots of a Wishart matrix
- A New Analysis of Variance Model for Non-Additive Data
- Matrix Approximation and Latent Roots
- THE INTERPRETATION OF INTERACTIONS IN FACTORIAL EXPERIMENTS
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