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Journal ArticleDOI

Designs Robust Against Presence of an Outlier in an Analysis of Covariance Model

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TLDR
In this article, an attempt has been made to find robust designs in a block design set-up with covariates, when there is one single wild observation in the study variable.
Abstract
Presence of one or more aberration in the observations affects inference procedure in statistical analysis. Block designs robust against presence of aberrations can be found in the literature for both regression and block design set-ups. In this paper, an attempt has been made to find robust designs in a block design set-up with covariates, when there is one single wild observation in the study variable. Specifically, effects on the estimation of a full set of orthonormal treatment contrasts and that on the estimation of covariate parameters have been considered.

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References
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Journal ArticleDOI

On Robust Designs

TL;DR: In this paper, the robustness of block designs for the problem of estimation of any full set of orthonormal varietal contrasts is examined, and an attempt has been made to examine their robustness against the presence of wild observations.
Journal ArticleDOI

Optimal experimental designs for models with covariates

TL;DR: In this paper, the authors investigate the underlying combinatorial problems in the context of CRD, RBD and BIBD in order to accommodate maximum number of covariates.
Journal ArticleDOI

Cyclic designs for a covariate model

TL;DR: In this article, a linear model with one treatment at V levels and first order regression on K continuous covariates with values on a K -cube is considered, where the allocation matrix of each treatment level (for short, allocation matrix ) is obtained through cyclic permutation of the columns of the first treatment level.
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