Robust ANOVA: An Illustrative Study in Horticultural Crop Research
An attempt has been made in the present communication to elucidate the efficacy of robust ANOVA methods to analyse horticultural field experimental data in the presence of outliers. Results obtained fortify the use of robust ANOVA methods as there was substantiate reduction in error mean square, and hence the probability of committing Type I error, as compared to the regular approach.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099174Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1846
 V. Barnnet, and T. Lewis, Outliers in statistical data, Wiley, New York, 3rd edition. 1984.
 L. Bhar and V. K Gupta, “A useful statistic for studying outliers in experimental designs”, Sankhya, B63 (2001), pp. 338-350.
 R. J. Carroll, “Robust methods for factorial experiments with outliers”, Appl. Stat., 29(1980), pp. 246-251.
 E. M. Chi, “M-estimation in cross- over trials. Biometrics”,50(1994), pp. 486-493.
 R.D. Cook,” Detection of influential observation in linear regression”, Technometrics, 19(1977), pp. 15-18.
 R. Johnson, Applied Multivariate Statistical Analysis. Prentice Hall. 1992.
 P.J. Huber, “Robust estimation of location parameter”,. Ann. Math. Statist.35(1964), pp.73-101.
 P.J. Huber, “Robust regression: Asymptotic, conjectures, and Monte carlo”. Ann. Stat., 1(1973), pp. 799-821
 P. W. Holland and R. E. Welsch, “Robust regression using iterative reweighted least-squares. Communications in statistics- Theoryand Methods”, A6(1977), pp. 813-827.
 D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 3rd edition, John Wiley and Sons, Inc, New York. 2001.
 R.K. Paul and L.M. Bhar, “M-estimation in block design”, Journal of Indian Society of agril. Stat., 65 (3) (2011), pp. 323-330.
 R. Parsad, V.K. Gupta, R. Srivastava, P. K. Batra, A. Kaur and P. Arya, “A diagnostic study of design and analysis of field experiments”, Technical Report, IASRI, New Delhi. 2004.