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SSA Canberra Branch Meetings

KNIBBS LECTURE

Our final meeting in 2023 is the annual Knibbs Lecture. It is a little earlier in the year than usual to take the opportunity to attract an eminent speaker on his way from Europe to a conference in New Zealand.

Date: Tuesday 21 November 2023

Time: 5:45pm AEDT   

Venue: Marie Reay Teaching Centre room 5.04, ANU (Marie Reay Teaching Centre level 5 - ANU), or via Zoom.

Zoom link:

https://anu.zoom.us/j/81416265570?pwd=MStjR2xndmN0cFBXTVUwcE1xN2gwZz09
RSVP is not required. Full zoom details given at the end of the email.

Speaker: Hans-Peter Piepho, Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany

Topic: A world of differences

Abstract:

A lot of applied research is comparative in nature, meaning that the focus is on differences between groups or treatments. This is also true in my main field of application, agriculture. Differences further play a key role in the mechanics of a number of statistical methods. In this talk, I will provide an overview of recent work I have been involved in, all of which makes use of differences in some way. The applications cover experimental design, recovery of inter-block information, network meta-analysis, heritability, goodness of fit for GLMMs, and spatial methods for field trials. As will be pointed out in my talk, there are interesting inter-connections between these different applications.

Biography:

Hans-Peter Piepho was appointed Professor of Biostatistics at the University of Hohenheim, Stuttgart, Germany in 2001. He has been working as an applied statistician in agricultural research for more than 30 years. His main interests are related to statistical procedures as needed in plant genetics, plant breeding and cultivar testing. Recent interests include envirotype- and marker-enabled breeding, spatial methods for field trials and experimental design for various applications including two-phase experiments and multi-environment trials. Further areas of interest include network meta-analysis and measure of goodness of fit for generalized linear mixed models.