Title
Model-based data assessment for terrestrial carbon processes: implications for sampling strategy in FACE experiments
Abstract
The value of different types of data in the estimation of different carbon transfer parameters is investigated. A carbon accounting model is used with different observation operators to generate data. The effectiveness of the inversion is assessed by observing relative errors of estimators and likelihood ratios. It is demonstrated that for an observation operator that relative errors vary widely with the sample test problems. An effective strategy to test types of data is to test the effectiveness of corresponding observation operators on an ensemble of sample problems for which parameters are selected from the space of admissible parameters. The selection is carried out under the assumption that the test parameters themselves are random variables uniformly distributed over the space of admissible parameters.
Year
DOI
Venue
2005
10.1016/j.amc.2004.07.016
Applied Mathematics and Computation
Keywords
Field
DocType
random variable,relative error,likelihood ratio
Random variable,Inversion (meteorology),Parameter space,Operator (computer programming),Sampling (statistics),Estimation theory,Statistics,Approximation error,Mathematics,Estimator
Journal
Volume
Issue
ISSN
167
1
0096-3003
Citations 
PageRank 
References 
4
0.93
1
Authors
2
Name
Order
Citations
PageRank
Luther White1167.80
Yiqi Luo2145.33