Record Details

Robust analysis of agricultural field experiments

Indian Agricultural Research Journals

View Archive Info
 
 
Field Value
 
Title Robust analysis of agricultural field experiments
 
Creator PAUL, RANJIT KUMAR
BHAR, LALMOHAN
PANWAR, SANJEEV
KUMAR, ANIL
 
Subject Agricultural experiments, Block design, Least Median of Squares estimation, Outlier
 
Description 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.
 
Publisher The Indian Journal of Agricultural Sciences
 
Contributor —
 
Date 2015-01-19
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/45998
 
Source The Indian Journal of Agricultural Sciences; Vol 85, No 1 (2015)
0019-5022
 
Language eng
 
Relation http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/45998/19932
 
Rights Copyright (c) 2015 The Indian Journal of Agricultural Sciences