We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Sequential Monte Carlo Approach for MLE in a Plant Growth Model.
- Authors
Trevezas, Samis; Cournède, Paul-Henry
- Abstract
Parametric identification of plant growth models formalized as discrete dynamical systems is a challenging problem due to specific data acquisition (system observation is generally done with destructive measurements), non-linear dynamics, model uncertainties and high-dimensional parameter space. In this study, we present a novel idea of modeling plant growth in the framework of non-homogeneous hidden Markov models (Cappé, Moulines, and Rydén ), for a certain class of plants with known organogenesis (structural development). Unknown parameters of the models are estimated via a stochastic variant of a generalized EM (Expectation-Maximization) algorithm and approximate confidence intervals are given via parametric bootstrap. The complexity of the model makes both the E-step (expectation step) and the M-step (maximization step) non-explicit. For this reason, the E-step is approximated via a sequential Monte Carlo procedure (sequential importance sampling with resampling) and the M-step is separated into two steps (Conditional-Maximization), where before applying a numerical maximization procedure (quasi-Newton type), a large subset of unknown parameters is updated explicitly conditioned on the other subset. A simulation study and a case-study with real data from the sugar beet are considered and a model comparison is performed based on these data. Appendices are available online.
- Subjects
MONTE Carlo method; MEDIATED learning experience; DYNAMICAL systems; CONFIDENCE intervals; PLANT growth; QUASI-Newton methods
- Publication
Journal of Agricultural, Biological & Environmental Statistics (JABES), 2013, Vol 18, Issue 2, p250
- ISSN
1085-7117
- Publication type
Article
- DOI
10.1007/s13253-013-0134-1