Browsing by Person "Williams, Emlyn R."

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Guest editors’ introduction to the special issue on “Recent advances in design and analysis of experiments and observational studies in agriculture”
(2020) Piepho, Hans-Peter; Tempelman, Robert J.; Williams, Emlyn R.
The Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture covers a select set of topics currently of primary importance in the field. Efficient use of resources in agricultural research, as well as valid statistical inference, requires good designs, and this special issue boasts seven papers providing both review and cutting-edge methodology for the purpose. A broad range of methods for analysis of data arising in different branches agricultural research is covered in another five exciting papers. This special issue highlights the importance of and opportunities for applied statistics in agriculture.
Linear variance, P-splines and neighbour differences for spatial adjustment in field trials: how are they related?
(2020) Boer, Martin P.; Piepho, Hans-Peter; Williams, Emlyn R.
Nearest-neighbour methods based on first differences are an approach to spatial analysis of field trials with a long history, going back to the early work by Papadakis first published in 1937. These methods are closely related to a geostatistical model that assumes spatial covariance to be a linear function of distance. Recently, P-splines have been proposed as a flexible alternative to spatial analysis of field trials. On the surface, P-splines may appear like a completely new type of method, but closer scrutiny reveals intimate ties with earlier proposals based on first differences and the linear variance model. This paper studies these relations in detail, first focussing on one-dimensional spatial models and then extending to the two-dimensional case. Two yield trial datasets serve to illustrate the methods and their equivalence relations. Parsimonious linear variance and random walk models are suggested as a good point of departure for exploring possible improvements of model fit via the flexible P-spline framework.