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Research Interests
Steven Gilmour's research is mostly on the statistical theory, methodology and applications of the design and analysis of experiments. Much of his research is on experiments with complex treatment structures, such as those with many variables (factorial designs), those with continuous levels (especially leading to nonlinear models) and those with several continuous variables (response surface methodology). Problems of choice of treatments, allocation of treatments to experimental units and links between design and modelling are all of importance to experimenters and lead to interesting statistical work.
Some particular areas of recent and current research are:
Multi-stratum designs, in which practical restrictions on the randomisation lead to information appearing at different levels of experimental unit from different treatment factors.
Multi-objective optimal design in which designs are sought which meet the multiple practical requirements that experimenters usually have.
Designs for multifactor nonlinear models, especially hybrid models in which mechanistic information is used to describe the effects of one or more factors on the response, but purely empirical models are used for the other factors' effects.
Designs for connected experimental units, where the usual assumption of treatment-unit additivity is not plausible and the design and modelling have to allow for this.
Factorial design which are good under model uncertainty, when it is not known which effects are expected before the experiment is run.
Design-based modelling of data from experiments, which is driven by the randomisation carried out, as well as what is know about the effects of treatments a priori.
These methods are applicable to experiments in many fields of application, but Steve has been particularly involved with applications in healthcare, pharmaceuticals, biochemistry, food, agrochemicals, biochemical engineering, horticulture and market research.
Steven Gilmour's research is mostly on the statistical theory, methodology and applications of the design and analysis of experiments. Much of his research is on experiments with complex treatment structures, such as those with many variables (factorial designs), those with continuous levels (especially leading to nonlinear models) and those with several continuous variables (response surface methodology). Problems of choice of treatments, allocation of treatments to experimental units and links between design and modelling are all of importance to experimenters and lead to interesting statistical work.
Some particular areas of recent and current research are:
Multi-stratum designs, in which practical restrictions on the randomisation lead to information appearing at different levels of experimental unit from different treatment factors.
Multi-objective optimal design in which designs are sought which meet the multiple practical requirements that experimenters usually have.
Designs for multifactor nonlinear models, especially hybrid models in which mechanistic information is used to describe the effects of one or more factors on the response, but purely empirical models are used for the other factors' effects.
Designs for connected experimental units, where the usual assumption of treatment-unit additivity is not plausible and the design and modelling have to allow for this.
Factorial design which are good under model uncertainty, when it is not known which effects are expected before the experiment is run.
Design-based modelling of data from experiments, which is driven by the randomisation carried out, as well as what is know about the effects of treatments a priori.
These methods are applicable to experiments in many fields of application, but Steve has been particularly involved with applications in healthcare, pharmaceuticals, biochemistry, food, agrochemicals, biochemical engineering, horticulture and market research.
研究兴趣
论文共 122 篇作者统计合作学者相似作者
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Harry Coppock,George Nicholson,Ivan Kiskin, Vasiliki Koutra, Kieran Baker,Jobie Budd,Richard Payne,Emma Karoune,David Hurley,Alexander Titcomb,Sabrina Egglestone, Ana Tendero Cañadas,
Nature Machine Intelligenceno. 2 (2024): 229-242
Wenjing Wang, Kieran Baker,Chianna Umamahesan,Steven Gilmour,Andre Charlett,David Taylor,Allan H. Young,R. John Dobbs,Sylvia M. Dobbs
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JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICSno. 3 (2023): 526-548
Davide Pigoli, Kieran Baker,Jobie Budd, Lorraine Butler,Harry Coppock,Sabrina Egglestone,Steven G. Gilmour,Chris Holmes,David Hurley,Radka Jersakova,Ivan Kiskin, Vasiliki Koutra,
arxiv(2023)
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Jobie Budd, Kieran Baker,Emma Karoune,Harry Coppock,Selina Patel, Ana Tendero Cañadas,Alexander Titcomb,Richard Payne,David Hurley,Sabrina Egglestone, Lorraine Butler,Jonathon Mellor,
arxiv(2023)
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Econometrics and Statistics (2021): 138-154
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