Selection criteria for linear regression models to estimate individual tree biomasses in the Atlantic Rain Forest, Brazil

Carlos Roberto Sanquetta1, Ana Paula Dalla Corte1, Alexandre Behling1, Luani Rosa de Oliveira Piva2, Sylvio Péllico Netto1, Aurélio Lourenço Rodrigues2, Mateus Niroh Inoue Sanquetta2
1Forest Science Department, Federal University of Paraná, Curitiba, Brazil
2Graduate Programme in Forestry, Federal University of Paraná, Curitiba, Brazil

Tóm tắt

Từ khóa


Tài liệu tham khảo

Sanquetta CR, Corte AP, da Silva F. Biomass expansion factor and root-to-shoot ratio for Pinus in Brazil. Carbon Bal Manag. 2011. https://doi.org/10.1186/1750-0680-6-6 .

Soares P, Tomé M. Analysis of the effectiveness of biomass expansion factors to estimate stand biomass. In: Hasenauer H, Makela A, editors. Modeling forest production. Vienna: University of Natural Resources and Applied Life Sciences; 2004. p. 368–74.

Kadane JB, Lazar NA. Methods and criteria for model selection. J Am Stat Assoc. 2004. https://doi.org/10.1198/016214504000000269 .

Linhart H, Zucchini W. Finite sample selection criteria for multinomial models. Stat Hefte. 1986. https://doi.org/10.1007/bf02932566 .

McQuarrie AD, Tsai C-L. Regression and time series model selection. 1st ed. Singapore: World Scientific Publishing Company; 1998.

Forster MR. Key concepts in model selection: performance and generalizability. J Math Psychol. 2000. https://doi.org/10.1006/jmps.1999.1284 .

Zucchini W. An introduction to model selection. J Math Psychol. 2000. https://doi.org/10.1006/jmps.1999.1276 .

Lahiri P. Model selection. Columbus: Institute of Mathematical Statistics; 2001.

Kuha J, AIC and BIC. Comparisons of assumptions and performance. Sociol Methods Res. 2004. https://doi.org/10.1177/0049124103262065 .

Müller S, Scealy JL, Welsh AH. Model selection in linear mixed models. Stat Sci. 2013. https://doi.org/10.1214/12-sts410 .

Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. Berlin: Springer, Science & Business Media; 2003.

Aitkin MA, Francis B, Hinde J. Statistical modelling in GLIM 4. 2nd ed. Oxford: Claredon Press; 2005.

Johnson JB, Omland KS. Model selection in ecology and evolution. Trends Ecol Evol. 2004. https://doi.org/10.1016/j.tree.2003.10.013 .

Tarald OK. Cautionary note about R2. Am Stat. 1985. https://doi.org/10.2307/2683704 .

Anscombe FJ. Graphs in statistical analysis. Am Stat. 1973. https://doi.org/10.2307/2682899 .

Vismara EdS. Mensuração da biomassa e construção de modelos para construção de equações de biomassa: Universidade de São Paulo; 2016.

Gujarati DN, Porter D. Basic econometrics. 5th ed. Bostos: McGraw-Hill Education; 2009.

Vanclay JK. Modelling forest growth and yield: applications to mixed tropical forests. 1st ed. Wallingford: CAB International; 1994.

Weisberg S. Applied linear regression. New York: Wiley; 2005.

Akaike H. Information theory as an extension of the maximum likelihood principle. In: Petrov BN, Csaki F, editors. Proceedings of the second international symposium on information theory. Budapeste: Akademiai Kiado; 1973. p. 267–81.

Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–4.

Sanquetta CR, Balbinot R. Métodos de determinação de biomassa florestal. In: Sanquetta CR, Watzlawick LF, Balbinot R, Ziliotto MAB, Gomes FS, editors. As Florestas e o Carbono. Curitiba: UFPR Press; 2002. p. 119–40.

Belsley DA, Kuh E, Welsch RE. Regression diagnostics: identifying influential data and sources of collinearity. J Market Res. 1980. https://doi.org/10.2307/3150985 .

Cook RD, Weisberg S. Residuals and influence in regression. New York: Chapman and Hall; 1982.

Doyle J. Model selection procedures and their error-reduction targets. 2011. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1789907 . Accessed 15 July 2018.

Figueiredo Filho DB, Júnior JAS, Rocha EC. What is R2 all about? Leviathan. 2011;3:60–8.

Dubbelman C. Disturbances in the linear model: estimation and hypothesis testing. Leiden: Martinus Nihjoff; 1978. p. 111.

Maddala G. Econometrics. New York: McGraw-Hill; 1977.

Maddala G, Lahiri K. Introduction to econometrics. New York: Wiley; 2001.

Ebbeler DH. On the probability of correct model selection using the maximum choice criterion. Int Econ Rev. 1975;16(2):516–20.

Greene WH. Econometric analysis. New Jersey: Prentice Hall International; 2003.

Rao CR, Wu Y, et al. On model selection. IMS Lect Monogr Ser. 2011. https://doi.org/10.1214/lnms/1215540960 .

Wooldridge JM. Introdução à econometria: uma abordagem moderna. São Paulo: Thomson Pioneira; 2006.

White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980. https://doi.org/10.2307/1912934 .

Frisch R. Statistical confluence analysis by means of complete regression systems. Nord Stat J. 1934;5:1–97.

Huang CJ, Bolch BW. On the testing of regression disturbances for normality. J Am Stat Assoc. 1974;69(346):330–5.

Tukey JW. On the comparative anatomy of transformations. Ann Math Stat. 1957;28:602–32.

Box GE, Cox DR. An analysis of transformations. J R Stat Soc. 1964;26:211–52.

Box GE, Watson GS. Robustness to non-normality of regression tests. Biometrika. 1962;49(1–2):93–106.