Measurement error in a random walk model with applications to population dynamics

Biometrics

Volumn 62, Issue 4, 2006, Pages 1178-1189

  Staudenmayer, John ^a   Buonaccorsi, John P ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

Ecology; Error in variables; Heteroskedasticity; Observation error; Pseudo estimator; Response error; Time series

Indexed keywords

DYNAMICS; METHOD OF MOMENTS; POPULATION DISTRIBUTION; POPULATION DYNAMICS; POPULATION STATISTICS; RANDOM PROCESSES; SAMPLING;

DYNAMICS MODELS; ERROR IN VARIABLE; HETEROSKEDASTICITY; MEASUREMENT ERROR MODELS; OBSERVATION ERRORS; PSEUDO-ESTIMATOR; RANDOM WALK MODELING; RESPONSE ERROR; TIMES SERIES; UNCERTAINTY;

UNCERTAINTY ANALYSIS;

ABUNDANCE; ERROR ANALYSIS; NUMERICAL MODEL; POPULATION DYNAMICS; POPULATION MODELING; RANDOM WALK METHOD;

ARTICLE; POPULATION DYNAMICS; SAMPLING ERROR; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 33845487964     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2006.00615.x     Document Type: Article

References (27)

1. Brockwell, P. J. and Davis, R. A. (2002). Introduction to Time Series and Forecasting, 2nd edition. New York: Springer.
2. Buonaccorsi, J. P., Staudenmayer, J. W., and Carreras, M. (2006). Modeling observation error and its effects in a random walk/extinction model. Theoretical Population Biology, in press.
3. Calder, C., Lavine, M., Muller, P., and Clark, J. S. (2003). Incorporating multiple sources of stochasticity into dynamic population models. Ecology 84, 1395-1402.
4. Carroll, R., Ruppert, D., and Stefanski, L. (1995). Measurement Error in Non-Linear Models. London: Chapman Hall.
5. Casella, G. and Berger, R. L. (2002). Statistical Inference. North Scituate, Massachusetts: Duxbury Press.
6. Dennis, B., Munolland, P. L., and Scott, J. M. (1991). Estimation of growth and extinction parameters for endangered species. Ecological Monographs 61, 115-143.
7. DeValpine, P. (2003). Better inferences from population-dynamics experiments using Monte Carlo state-space likelihood methods. Ecology 84, 3064-3077.
8. DeValpine, P. and Hastings, A. (2002). Fitting population models incorporating process noise and observation error. Ecological Monographs 72, 57-76.
9. Graybill, F. A. (1976). Theory and Application of the Linear Model. North Scituate, Massachusetts: Duxbury Press.
10. Holmes, E. E. (2001). Estimating risks in declining populations with poor data. Proceedings of the National Academy of Sciences of the United States of America 98, 5072-5077.
11. Holmes, E. E. (2004). Beyond theory to application and evaluation: Diffusion approximations for population viability analysis. Ecological Applications 14, 1272-1293.
12. Holmes, E. E. and Fagan, W. F. (2002). Validating population viability analysis for corrupted data sets. Ecology 83, 2379-2386.
13. Kenward, M. G. and Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983-997.
14. Lindley, S. T. (2003). Estimation of population growth and extinction parameters from noisy data. Ecological Applications 13, 806-813.
15. Ludwig, D. (1999). Is it meaningful to estimate a probability of extinction? Ecology 80, 298-310.
16. McCallum, H. (2000). Population Parameters: Estimation for Ecological Models. Oxford: Blackwell Science.
17. McCulloch, C. E. and Searle, S. R. (2001). Generalized, Linear and Mixed Models. New York: Wiley.
18. McNamara, J. M. and Harding, K. C. (2004). Measurement error and estimates of population extinction risk. Ecology Letters 7, 16-20.
19. Meier, E. and Fagan, W. F. (2000). Will observation error and biases ruin the use of simple extinction models? Conservation Biology 14, 148-154.
20. Morris, W. F. and Doak, D. F. (2002). Quantitative Conservation Biology: Theory and Practice of Population Variability Analysis. Sunderland, Massachusetts: Sinauer Associates.
21. Parke, W. R. (1986). Pseudo maximum likelihood estimation: The asymptotic distribution. Annals of Statistics 14, 355-357.
22. Seber, G. A. F. (1987). Estimation of Animal Abundance, 2nd edition. High Wycombe, U.K.: Charles Griffin.
23. Shenk, T., White, G., and Burnham, P. (1998). Samplingvariance effects on detecting density dependence from temporal trends in natural populations. Ecological Monographs 68, 445-463.
24. Solow, A. R. (1998). On fitting a population model in the presence of observation error. Ecology 79, 1463-1466.
25. Solow, A. R. (2001). Observation error and the detection of delayed density dependence. Ecology 82, 3263-3264.
26. Staples, D. F., Taper, M. L., and Dennis, B. (2004). Estimating population trend and process variation for PVA in the presence of sampling error. Ecology 85, 923-929.
27. USFWS (U.S. Fish and Wildlife Service). (1994). Whooping Crane Recovery Plan. Albuquerque: U.S. Fish and Wildlife Service.