Journal Article10.2307/2346566
Estimating Missing Values in Experiments
J. A. John,Philip Prescott +1 more
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About: This article is published in Applied statistics. The article was published on 01 Jun 1975. The article focuses on the topics: Missing data.
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Citations
An application of multivariate ratio methods for the analysis of a longitudinal clinical trial with missing data.
TL;DR: This paper presents an analysis of a longitudinal multi-center clinical trial with missing data that illustrates the application, the appropriateness, and the limitations of a straightforward ratio estimation procedure for dealing with multivariate situations in which missing data occur at random and with small probability.
45
On Testing for Two Outliers or One Outlier in Two-Way Tables
J. A. John,N. R. Draper +1 more
TL;DR: The authors proposed a two-stage test for the presence of two outliers or one outlier in two-way tables, where the test statistics are estimated by Monte Carlo generations, and approximations to the percentage points are suggested.
39
The Analysis of Designed Experiments with Missing Observations
TL;DR: In this article, the relationship between exact and iterative procedures for analysing designed experiments with missing observations has been examined in a common mathematical framework, and the most common procedure is to use an iterative approach similar to that proposed by Healy and Westmacott (1956).
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Testing for Three or Fewer Outliers in Two-Way Tables
N. R. Draper,J. A. John +1 more
TL;DR: In this paper, a test statistic QK was considered and was shown to be the sum of squares of successive adjusted normalized uncorrelated residuals, and approximations to these percentage points were suggested.
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Design issues and analysis of experiments in nanomanufacturing
Harriet Black Nembhard,Navin N. Acharya,Mehmet Aktan,Seong H. Kim +3 more
- 01 Jan 2013
TL;DR: In this article, the authors identify the importance of statistically-based design of experiments (DOE) to the research and development of nanomanufacturing, and they address three cases that closely link DOE with the needs of nanomedicine.
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References
Missing Values in Experiments Analysed on Automatic Computers
TL;DR: A general technique for dealing with observations missing from block experiments analysed on automatic computers that is applicable to any analysis in which least‐squares estimates are derived.
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Iterative Procedures for Missing Values in Experiments
TL;DR: In this article, the authors gave an iterative missing value procedure in which a simple correction is subtracted from each estimated missing value at each iteration, and they showed that it is advantageous to replace the simple correction by a multiple m of it, m > 1.
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Missing Data in Quantitative Designs
TL;DR: A new procedure for use in factorial designs is suggested, and its properties examined, which is compared with existing procedures in both a theoretical and a practical light.
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