Referencias
Akaike, H. (1974). A new look at the statistical model identification.
IEEE Transactions on Automatic Control, 19(6),
716–723. https://doi.org/10.1109/TAC.1974.1100705
Andersson, S. (2017). Strong identification in econometric models.
Journal of Econometrics, 198(2), 245–260.
Bellman, R. (1970). Introduction to the mathematical theory of
control processes. Academic Press.
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression
diagnostics: Identifying influential data and sources of
collinearity. John Wiley & Sons.
Breusch, T. S., & Pagan, A. R. (1979). A simple test for
heteroscedasticity and random coefficient variation.
Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963
Burnham, K. P., & Anderson, D. R. (2002). Model selection and
multimodel inference: A practical information-theoretic approach
(2nd ed.). Springer.
Casella, G., & Berger, R. L. (2002). Statistical inference
(2nd ed.). Duxbury Press.
Cuddington, K., Fortin, M.-J., Gerber, L. R., Hastings, A., Liebhold,
A., O’Connor, M., & Ray, C. (2013). Process-based models are
required to manage ecological systems in a changing world.
Ecosphere, 4(2), 1–12. https://doi.org/10.1890/ES12-00178.1
Diepenbrock, W. (2000). Yield formation in field-grown brassica oleracea
l. Var. Botrytis: A review. Journal of Horticultural Science &
Biotechnology, 75(4), 395–408. https://doi.org/10.1080/14620316.2000.11511267
Dietze, M. C. (2017). Ecological forecasting. Princeton
University Press.
Durbin, J., & Watson, G. S. (1950). Testing for serial correlation
in least squares regression: i. Biometrika, 37(3/4),
409–428. https://doi.org/10.2307/2332391
Fox, J. (2015). Applied regression analysis and generalized linear
models (3rd ed.). SAGE Publications.
Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson.
Hamilton, J. D. (1994). Time series analysis. Princeton
University Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements
of statistical learning: Data mining, inference, and prediction
(2nd ed.). Springer.
Hatfield, J. L., & Prueger, J. H. (2015). Temperature extremes:
Effect on plant growth and development. Weather and Climate
Extremes, 10, 4–10. https://doi.org/10.1016/j.wace.2015.08.001
Itô, K. (1951). On stochastic differential equations. Memoirs of the
American Mathematical Society, 4, 1–51. https://doi.org/10.1090/memo/0004
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality,
homoscedasticity and serial independence of regression residuals.
Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
Kloeden, P. E., & Platen, E. (1992). Numerical solution of
stochastic differential equations (Vol. 23). Springer-Verlag. https://doi.org/10.1007/978-3-662-12616-5
Mao, X. (2007). Stochastic differential equations and
applications (2nd ed.). Horwood Publishing.
Miao, H., Xia, X., Perelson, A. S., & Wu, H. (2011). On
identifiability of nonlinear ODE models and applications in viral
dynamics. SIAM Review, 53(1), 3–39. https://doi.org/10.1137/090757009
Milstein, G. N. (1995). Numerical integration of stochastic
differential equations (Vol. 313). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-8455-5
Mohammed, S.-E. A. (1984). Stochastic functional differential
equations. 99.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021).
Introduction to linear regression analysis (6th ed.). John
Wiley & Sons.
Ogle, K., Barber, J. J., Barron-Gafford, G. A., Bentley, L. P., Cable,
J. M., Huxman, T. E., Loik, M. E., & Tissue, D. T. (2015).
Quantifying ecological memory in plant and ecosystem processes.
Ecology Letters, 18(3), 221–235. https://doi.org/10.1111/ele.12399
Platen, E. (1999). An introduction to numerical methods for stochastic
differential equations. Acta Numerica, 8, 197–246. https://doi.org/10.1017/S0962492900002941
Revuz, D., & Yor, M. (1999). Continuous martingales and brownian
motion (3rd ed., Vol. 293). Springer.
Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Furze, M.
E., Seyednasrollah, B., et al. (2018). Tracking vegetation phenology
across diverse biomes using version 2.0 of the PhenoCam dataset.
Scientific Data, 6(1), 222. https://doi.org/10.1038/s41597-018-0164-4
Särkkä, S., & Solin, A. (2019). Applied stochastic differential
equations. Cambridge University Press. https://doi.org/10.1017/9781108186735
Schwarz, G. (1978). Estimating the dimension of a model. The Annals
of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
Shoji, I., & Ozaki, T. (1998). Estimation for nonlinear stochastic
differential equations by a local linearization method. Stochastic
Analysis and Applications, 16(4), 733–752. https://doi.org/10.1080/07362999808809559
Taiz, L., Zeiger, E., Møller, I. M., & Murphy, A. (2022). Plant
physiology and development (7th ed.). Oxford University Press.
Thomas, H., & Ougham, H. (2014). The stay-green trait. Journal
of Experimental Botany, 65(14), 3889–3900. https://doi.org/10.1093/jxb/eru033
Volterra, V. (1931). Théorie mathématique de la lutte pour la
vie. Gauthier-Villars.
White, H. (1980). A heteroskedasticity-consistent covariance matrix
estimator and a direct test for heteroskedasticity.
Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934
Wilkinson, D. J. (2018). Stochastic modelling for systems
biology (3rd ed.). CRC Press.
Wilks, D. S. (2019). Statistical methods in the atmospheric
sciences (4th ed.). Elsevier.