论文信息 - ON FITTING A POPULATION MODEL IN THE PRESENCE OF OBSERVATION ERROR

ON FITTING A POPULATION MODEL IN THE PRESENCE OF OBSERVATION ERROR

Observation error can lead to substantial bias in estimating the parameters of a population model. While maximum-likelihood estimation is possible in principle, it can be extremely difficult in practice due to the complicated behavior of the likelihood function. This note describes a simple method that can be used to fit a population model in the presence of observation error. The method is illustrated using the discrete-time logistic model.

Andrew R. Solow | A. Solow

[1] R. May,et al. Stability and Complexity in Model Ecosystems , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[2] T. Royama,et al. Analytical Population Dynamics , 1994, Population and Community Biology Series.

[3] J. R. Cook,et al. Simulation-Extrapolation Estimation in Parametric Measurement Error Models , 1994 .

[4] Robert M. May,et al. Simple mathematical models with very complicated dynamics , 1976, Nature.

[5] Andrew R. Solow. Fitting Population Models to Time Series Data , 1995 .

[6] L. Mark Berliner,et al. Likelihood and Bayesian Prediction of Chaotic Systems , 1991 .

[7] G. Seber. The estimation of animal abundance and related parameters , 1974 .