Robust analysis of agricultural field experiments


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Authors

  • RANJIT KUMAR PAUL Scientist, Division of Statistical Genetics, Indian Agricultural Statistics Research Institute, New Delhi 110 012
  • LALMOHAN BHAR Principal Scientist, Division of Design of Experiments, Indian Agricultural Statistics Research Institute, New Delhi 110 012
  • SANJEEV PANWAR Scientist, Division of Forecasting and Agricultural System Modelling, Indian Agricultural Statistics Research Institute, New Delhi 110 012
  • ANIL KUMAR Indian Agricultural Statistics Research Institute, New Delhi 110 012

https://doi.org/10.56093/ijas.v85i1.45998

Keywords:

Agricultural experiments, Block design, Least Median of Squares estimation, Outlier

Abstract

Agricultural data generated from designed experiments are also prone to occurrence outliers. It is well known that Least Squares (LS) model can be distorted even by a single outlying observation. An outlier is one that appears to deviate markedly from the other members of the sample in which it occurs. The sources of influential subsets are diverse. Rousseeuw (1984) introduced a robust method known as Least Median of Squares (LMS) for linear regression models. By this method, the median of squares errors is minimized in order to obtain parameter estimates. It turns out that this estimator is very robust with respect to outliers. Since it focuses on the median residual, up to half of the observations can disagree without masking a model that fits the rest of the data. Therefore, the breakdown point of this estimator is 50%, the highest possible value. In the present investigation, this method is applied to analyze the data set containing outlying observations generated from agricultural field experiments. The data sets for the present investigation have been taken from Agricultural Field Experiments Information System, IASRI, New Delhi.

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References

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Submitted

2015-01-16

Published

2015-01-16

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Section

Articles

How to Cite

PAUL, R. K., BHAR, L., PANWAR, S., & KUMAR, A. (2015). Robust analysis of agricultural field experiments. The Indian Journal of Agricultural Sciences, 85(1), 55-58. https://doi.org/10.56093/ijas.v85i1.45998
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