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The most known of these methods is that based on kernel estimator [11].
Based on kernel estimator [11], the smoothing parameter is calculated as (see Appendix C) begin{array}rcl@ h_{text{opt}}&=&left(frac{R(K)}{{mu_{2}}^{2}(K Rleft(f^{primeprime}right)}right)^{1/5}.N^{-1/5}, end{array} (18).
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Some of the most interesting parameters generated in the wildlife literature to describe space use include calculations of distance traveled, tortuosity of movement and core areas based on Kernel estimators.
The goal is to prove large deviations limit theorems for statistics, which are based on kernel density estimator and which are designed for symmetry testing.
In this context, this article presents a methodology to directly reconstruct this functional based on kernel density estimators.
Additionally, PDF estimators based on kernel functions are also developed.
The mathematical tools are based on the kernel estimators technique.
The weights are determined based on the kernel density estimation, which can be regarded as a Nadaraya-Watson estimator, i.e., a kind of local constant regression [7].
To this end, we first define the local kernel alignment based on centralized kernel alignment.
RDBC criterion is based on robust estimator which is biased.
Hence, it is the aim of this paper to propose a PDF estimator based on a smooth kernel PDF estimator that assumes known transition times.
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