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Analysis of variance in experimental design with nonnormal error distributions
Date
2001-01-01
Author
Senoglu, B
Tiku, ML
Metadata
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We consider a two-way classification model with interaction and assume that the errors have a location-scale nonnormal distribution. From an application of the modified likelihood estimation, we obtain efficient and robust estimators of the parameters. We define F statistics for testing main effects and interaction. We analyze the Box-Cox data and show that the method developed in this paper gives accurate results besides being easy theoretically and computationally.
Subject Keywords
Experimental design
,
Nonnormality
,
Block effects
,
Interaction
,
Skewness
,
Generalized logistic
,
Weibull
,
Robustness
URI
https://hdl.handle.net/11511/65032
Journal
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
DOI
https://doi.org/10.1081/sta-100104748
Collections
Department of Statistics, Article
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The estimation of coefficients in a simple autoregressive model is considered in a supposedly difficult situation where the innovations have an asymmetric distribution. Two distributions, gamma and generalized logistic, are considered for illustration. Closed form estimators are obtained and shown to be efficient and robust. Efficiencies of least squares estimators are evaluated and shown to be very low. This work is an extension of that of Tiku, Wong and Bian [1] who give solutions for a simple AR(I) model
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One-way and two-way classification models in experimental design for both balanced and unbalanced cases are considered when the errors have Generalized Secant Hyperbolic distribution. Efficient and robust estimators for main and interaction effects are obtained by using the modified maximum likelihood estimation (MML) technique. The test statistics analogous to the normal-theory F statistics are defined to test main and interaction effects and a test statistic for testing linear contrasts is defined. It is ...
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Senoglu, B; Tiku, ML (Wiley, 2002-01-01)
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B. Senoglu and M. Tiku, “Analysis of variance in experimental design with nonnormal error distributions,”
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
, pp. 1335–1352, 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65032.