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Akaike Information Criterion and Fourth-Order Kernel Method for Line Transect Sampling (LTS) |
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PP: 267-271 |
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doi:10.18576/amis/100127
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Author(s) |
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Ali Algarni,
Ahmad Almutlg,
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Abstract |
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| Parametric and noparametric approaches were used to fit line transect data. Different parametric detection functions are
suggested to compute the smoothing parameter of the nonparametric fourth-order kernel estimator. Among the different candidate
parametric detection functions, the researcher suggests to use Akaike Information Criterion (AIC) to select the most appropriate one of
them to fit line transect data. More specifically, four different parametric models are considered in this research. Where as two models
were taken to satisfy the shoulder condition assumption, the other two do not. Once the appropriate model is determined, it can be used
to select the smoothing parameter of the nonparametric fourth-order kernel estimator. As the researcher expected, this technique leads
to improve the performances of the fourth-order kernel estimator. For a wide range of target densities, a simulation study is performed
to study the properties of the proposed estimators which show the superiority of the resulting proposed fourth-order kernel estimator
over the classical kernel estimator in most considered cases. |
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