NDLI: The practical use of semiparametric models in field trials

Content Provider	Springer Nature Link
Author	Durban, Maria Hackett, Christine A. McNicol, James W. Newton, Adrian C. Thomas, William T. B. Currie, Iain D.
Copyright Year	2003
Abstract	This article examines the practical use of semiparametric models in the analysis of field trials—that is, models with parameterized treatment effects and additive terms derived by a data-driven approach using a locally weighted running line smoother (loess). We discuss graphical methods to identify spatial structure in the data and model selection procedures to choose the degree of smoothing. Once the spatial part of the model has been chosen, hypotheses about the treatment effects may be tested. Semiparametric models are used to analyze two barley field trials exhibiting spatial trends. The first has a single experimental treatment and a row-column design. The second has a split-plot design, and we use a semiparametric model which accounts for the randomization at the different strata of this design. We compare the semiparametric analyses with classical analyses of variance and with alternative spatial models. We find that semiparametric models give a good insight into spatial variation in the field and can improve the precision of parameter estimates.
Starting Page	48
Ending Page	66
Page Count	19
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	8
Issue Number	1
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bootstrap F test Loess Model selection Smoothing Spatial analysis Split-plot Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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