Information in an observation in robust designs
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Publication:3664251
DOI10.1080/03610928208828302zbMath0516.62073OpenAlexW1990343688MaRDI QIDQ3664251
Publication date: 1982
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610928208828302
informationbalanced incomplete block designmissing valuesunavailable datarandomized block designlatin square designrobustness of designs
Design of statistical experiments (62K99) Robustness and adaptive procedures (parametric inference) (62F35)
Related Items (14)
A bayesian approach to detect informative observations in an experiment ⋮ EFFICIENCY OF PAIR-WISE TREATMENT COMPARISONS IN INCOMPLETE BLOCK EXPERIMENTS SUBJECT TO THE LOSS OF A BLOCK OF OBSERVATIONS ⋮ Multilevel Augmented Pairs Second-Order Response Surface Designs and Their Robustness to Missing Data ⋮ Robustness to the unavailability of data in the linear model, with applications ⋮ Factorial and response surface designs robust to missing observations ⋮ Balanced block designs robust against the loss of a single observation ⋮ Robustness of balanced incomplete block designs to randomly missing observations ⋮ Influential observations in view of design and inference ⋮ Robustness group divisible designs ⋮ Robustness of some balanced block designs ⋮ Robustness of balanced fractional \(2^ m\) factorial designs derived from simple arrays ⋮ \(D\)-optimal orthogonal array minus \(t\) run designs ⋮ Missing observations in Youden square designs ⋮ An implicit function based procedure for analyzing maximum likelihood estimates from nonidentically distributed data
Cites Work
- On robustness of optimal balanced resolution V plans
- Robustness of connected balanced block designs
- Robustness of balanced incomplete block designs
- Robust designs
- Balanced 2mfactorial designs of resolution v which allow search and estimation of one extra unknown effect, 4 ≤ m ≤ 8
- The robustness of chain block designs and coat-of-mail designs
- The Sequential Generation of $D$-Optimum Experimental Designs
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