Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees

Ecological Modelling

Volumn 191, Issue 1, 2006, Pages 118-130

  Sulkava, Mika ^a   Tikka, Jarkko ^a   Hollmén, Jaakko ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Coniferous tree; Foliar nutrition; Linear sparse regression; Prediction

Indexed keywords

REGRESSION ANALYSIS; VEGETATION;

CONIFEROUS TREES; FOLIAR NUTRITION; LINEAR SPARSE REGRESSION;

FORESTRY;

CONIFEROUS TREE; ECOLOGICAL MODELING; NUTRIENT; PHYTOCHEMISTRY; PREDICTION; REGRESSION;

PLANTS; REGRESSION ANALYSIS; TREES;

CONIFERALES;

EID: 28444472323     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2005.08.016     Document Type: Conference Paper

References (19)

1. C.M. Bishop Neural Networks for Pattern Recognition 1995 Oxford University Press New York
2. L. Breiman The little bootstrap and other methods for dimensionality selection in regression: X-fixed prediction error J. Am. Statist. Assoc. 87 419 1992 738 754
3. L. Breiman Better subset regression using the nonnegative garrotte Technometrics 37 4 1995 373 384
4. L. Breiman, and J.H. Friedman Predicting multivariate responses in multivariate regression J. R. Statist. Soc. B 59 1 1997 3 54
5. J.B. Copas Regression, prediction and shrinkage J. R. Statist. Soc. B 45 3 1983 311 354
6. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani Least angle regression Ann. Statist. 32 2 2004 407 499
7. J.H. Friedman Multivariate adaptive regression splines Ann. Statist. 19 1 1991 1 67
8. P. Good Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, Springer Series in Statistics 2nd ed 2000 Springer Verlag
9. W. Guthrie, J. Filliben, and A. Heckert Process modeling NIST/SEMATECH e-Handbook of Statistical Methods 2005 National Institute of Standards and Technology (Chapter 4). http://www.itl.nist.gov/div898/handbook/
10. J.F. Hair, R.E. Anderson, R.L. Tatham, and W.C. Black Multivariate Data Analysis 5th ed 1995 Prentice-Hall
11. M.H. Hansen, and B. Yu Model selection and the principle of minimum description length J. Am. Statist. Assoc. 96 454 2001 746 774
12. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics 2001 Springer Verlag
13. S. Haykin Neural Networks: A Comprehensive Foundation 1999 Prentice-Hall
14. A.E. Hoerl, and R.W. Kennard Ridge regression: biased estimation for nonorthogonal problems Technometrics 12 1 1970 55 67
15. S. Luyssaert, H. Raitio, and A. Fürst Forest nutrition on the Finnish and Austrian Level I plots in 1987-2000 Forest Condition in Europe - Results of the 2002 Large-scale Survey 2003 UNECE, EC Geneva, Brussels pp. 72-83 (Chapter 5)
16. S. Luyssaert, M. Sulkava, H. Raitio, and J. Hollmén Evaluation of forest nutrition based on large-scale foliar surveys: are nutrition profiles the way of the future? J. Environ. Monit. 6 2 2004 160 167
17. p Technometrics 15 4 1973 661 675
18. Stefan, K., Fürst, A., Hacker, R., Bartels, U., 1997. Forest foliar condition in Europe - results of large-scale foliar chemistry surveys 1995. Technical Report. EC, UN/ECE, Brussels, Geneva.
19. R. Tibshirani Regression shrinkage and selection via the lasso J. R. Statist. Soc. B 58 1 1996 267 288