Double Kernel Method Using Line Transect Sampling
DOI:
https://doi.org/10.17713/ajs.v41i2.177Abstract
A double kernel method as an alternative to the classical kernel method is proposed to estimate the population abundance by using line transect sampling. The proposed method produces an estimator that is essentially a kernel type of estimator use the kernel estimator twice to improve the performances of the classical kernel estimator. The feasibility of using bootstrap techniques to estimate the bias and variance of the proposed estimator is also addressed. Some numerical examples based on simulated and real data are presented. The results show that the proposed estimator outperforms existingclassical kernel estimator in most considered cases.
References
Barabesi, L. (2000). Local likelihood density estimation in line transect sampling. Environmetrics, 11, 413-422.
Barabesi, L. (2001). Local parametric density estimation methods in line transect sampling. Metron, 59, 21-37.
Buckland, S. T. (1992). Fitting density functions using polynomials. Applied Statistics, 41, 63-76.
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., and Thomas, L. (2001). Introduction to Distance Sampling. Oxford: Oxford University Press.
Burnham, K. P., Anderson, D. R., and Laake, J. L. (1980). Estimation of density from line transect sampling of biological populations. Wildlife Monograph(72).
Chen, S. X. (1996). A kernel estimate for the density of a biological population by using line-transect sampling. Applied Statistics, 45, 135-150.
Eberhardt, L. L. (1968). A preliminary appraisal of line transects. Journal of Wildlife Management, 32, 82-88.
Efron, B., and Tibshirani, R. (1993). An Introduction to the Bootstrap. New York: Chapman and Hall.
Eidous, O. M. (2005). On improving kernel estimators using line transect sampling. Communications in Statistics: Theory and Methods, 34, 931-941.
Eidous, O. M. (2009). Kernel method starting with half-normal detection function for line transect density estimation. Communications in Statistics: Theory and Methods, 38, 2366-2378.
Hayes, R. J., and Buckland, S. T. (1983). Radial distance models for line-transect method. Biometrics, 39, 29-42.
Jones, M. C., Linton, O., and Nielsen, J. P. (1995). A simple bias reduction method for density estimation. Biometrika, 82, 327-338.
Loftsgaarden, D., and Quesenberry, C. (1965). A nonparametric estimate of multivariate density function. Annals of Mathematical Statistics, 36, 1049-1051.
Mack, Y. P., and Quang, P. X. (1998). Kernel methods in line and point transect sampling. Biometrics, 54, 606-619.
Mack, Y. P., and Rosenblatt, M. (1979). Multivariate k-nearest neighbor density estimates. Journal of Multivariate Analysis, 9, 1-15.
Silverman, B. W. (1986). Density Estimation. London: Chapman and Hall.
Downloads
Published
How to Cite
Issue
Section
License
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.
