NDLI: Using SAEM to estimate parameters of models of response to applied fertilizer

Content Provider	Springer Nature Link
Author	Makowski, David Lavielle, Marc
Copyright Year	2006
Abstract	Many nonlinear functions have been used to predict crop yield response to applied fertilizer. Accurate estimates of the parameter values are required for the formulation of satisfactory fertilizer dose recommendations. In fertilizer trials, several yield measurements for different fertilizer doses are usually made at the same site and in the same year. Model errors are therefore unlikely to be independent for the same site-year. Nonlinear mixed effects models can be used for yield response data. We evaluated the performance of a recently proposed stochastic approximation of the EM algorithm (SAEM) for estimating the parameters of nonlinear mixed effects models. We used the SAEM method to estimate the parameters of four different nonlinear models, using a real dataset including 37 site-years of yield measurements. We then carried out simulations, to determine the bias and the root mean squared errors of the estimators. We compared the results obtained with SAEM with those obtained using a first-order conditional method. SAEM gave better results in most cases. The estimates produced by SAEM were less biased and less affected by initial values.
Starting Page	45
Ending Page	60
Page Count	16
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	11
Issue Number	1
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	EM Fertilization Hierarchical model Markov chain Monte Carlo Nonlinear mixed effects model Parameter estimation 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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