Preserving correlation while modelling diameter distributions

Canadian Journal of Forest Research

Volumn 34, Issue 1, 2004, Pages 221-232

  Robinson, Andrew ^a  

^a UNIVERSITY OF IDAHO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; CORRELATION METHODS; DATA REDUCTION; ENVIRONMENTAL IMPACT; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS;

DIAMETER DISTRIBUTIONS; POPULATION PARAMETERS;

FORESTRY;

CORRELATION; DIAMETER; FORESTRY; MODELING;

EID: 1842684663     PISSN: 00455067     EISSN: None     Source Type: Journal    
DOI: 10.1139/x03-191     Document Type: Article

References (27)

1. Bailey, R.L., and Dell, T.R. 1973. Quantifying diameter distributions with the Weibull function. For. Sci. 19: 97-104.
2. Borders, B.E., Souter, R.A., Bailey, R.L., and Ware, K.D. 1987. Percentile-based distributions characterize forest stand tables. For. Sci. 33: 570-576.
3. Burk, T.E., and Newberry, J.D. 1984. A simple algorithm for moment-based recovery of Weibull distribution parameters. For. Sci. 30: 329-332.
4. Burnham, K.P., and Anderson, D.R. 1998. Model selection and inference: a practical Information-Theoretic approach. Springer-Verlag, New York.
5. Chaudry, M.A., and Ahmad, M. 1993. On a probability function useful in size modelling. Can. J. For. Res. 23: 1679-1683.
6. Cook, R.D., and Weisberg, S. 1999. Applied regression including computing and graphics. Wiley-Interscience, New York.
7. Dubey, S.D. 1967. Some percentile estimators for Weibull parameters. Technometrics, 9: 119-129.
8. Hafley, W.L., and Schreuder, H.T. 1977. Statistical distributions for fitting diameter and height data in even-aged stands. Can. J. For. Res. 7: 481-487.
9. Ihaka, R., and Gentleman, R.C. 1996. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5: 299-314.
10. Johnson, N.L., Kotz, S., and Balakrishnan, N. 1994. Continuous univariate distributions. 2nd ed. Wiley-Interscience, New York.
11. Kangas, A., and Maltamo, M. 2000. Performance of percentile based diameter distribution prediction and Weibull method in independent data sets. Silva Fenn. 34: 381-398.
12. Kuru, G.A., Whyte, A.G.D., and Woollons, R.C. 1992. Utility of reverse Weibull and extreme value density functions to refine diameter distribution growth estimates. For. Ecol. Manage. 48: 165-174.
13. Laird, N.M., and Ware, J.H. 1982. Random-effects models for longitudinal data. Biometrics, 38: 963-974.
14. Landsberg, J.J., and Waring, R.H. 1997. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For. Ecol. Manage. 95: 209-228.
15. Mabvurira, D., Maltamo, M., and Kangas, A. 2002. Predicting and calibrating diameter distributions of Eucalyptus grandis (Hill) Maiden plantations in Zimbabwe. New For. 23: 207-223.
16. Maltamo, M., and Kangas, A. 1998. Methods based on k-nearest neighbor regression in estimation of basal area diameter distribution. Can. J. For. Res. 28: 1107-1115.
17. Maltamo, M., Kangas, A., Uuttera, J., Torniainen, T., and Saramäki, J. 2000. Comparison of percentile based prediction methods and the Weibull distribution in describing the diameter distribution of heterogeneous scots pine stands. For. Ecol. Manage. 133: 263-274.
18. McCulloch, C.E., and Searle, S.R. 2001. Generalized, linear, and mixed models. Wiley-Interscience, New York.
19. Moeur, M., and Stage, A.R. 1995. Most similar neighbor: an improved sampling inference procedure for natural resource planning. For. Sci. 41: 337-359.
20. Moore, J.A., Mika, P.G., and Vander-Ploeg, J.L. 1991. Nitrogen fertilizer response of rocky mountain Douglas-fir by geographic area across the inland Northwest. West. J. Appl. For. 6: 91-94.
21. Pinheiro, J.C., and Bates, D.M. 2000. Mixed-effects models in S and Splus. Springer-Verlag, New York.
22. Reynolds, M.R., Burk, T.E., and Huang, W.C. 1988. Goodness-of-fit tests and model selection procedures for diameter distribution models. For. Sci. 34: 373-399.
23. Schabenberger, O., and Pierce, F.J. 2002. Contemporary statistical models in the plant and soil sciences. CRC Press, Boca Raton, Fla.
24. Venables, W.N., and Ripley, B.D. 2002. Modern applied statistics with S. 4th ed. Springer-Verlag, New York.
25. Wykoff, W.R., Crookston, N.L., and Stage, A.R. 1982. User's guide to the stand prognosis model. USDA For. Serv. Gen. Tech. Rep. GTR-INT 133.
26. Zarnoch, S.J., and Dell, T.R. 1985. An evaluation of percentile and maximum likelihood estimators of weibull parameters. For. Sci. 31: 260-268.
27. Zellner, A. 1962. An effcient method for estimating seemingly-unrelated regressions and tests for aggregation bias. J. Am. Stat. Assoc. 57: 348-368.