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In this article a new bandwidth selector based on the use of a bimodal kernel is proposed and investigated.
It is shown that the new bandwidth selector is quite useful for the tuning procedures of various other methods.
Furthermore, the proposed bandwidth selector itself proves to be quite effective when the errors are severely correlated.
This two-stage method can dramatically reduce the variability of the conventional unbiased cross-validation bandwidth selector.
This simple first-order extrapolation estimator is equivalent to the rescaled "bagging-CV" bandwidth selector in Hall and Robinson (2009) if one sets the bootstrap size equal to the fictional sample size.
Therefore, without significant loss of performance and knowing that it is the easiest to calculate and handle [34], the ASE is clearly an appropriate bandwidth selector.
Similar(51)
We define an empirical influence measure for data-driven bandwidth selectors and, through it, we study the sensitivity of the data-driven bandwidth selectors.
In addition, we also investigated the asymptotic properties of the proposed bandwidth selectors.
The bandwidth selectors used in this hazard assessment provide global estimates of density, in the sense that one bandwidth or bandwidth matrix is used to describe variation across the entire region.
Bivariate bandwidth selectors like the SAMSE method are extremely useful because, although they are mathematically complex, they find optimal bandwidths using the actual data locations, removing subjectivity from the process.
This means estimating the kernel density on 401 equally spaced points with a Gaussian kernel and with bandwidth estimated by Wand and Jones' oversmoothed kernel selector.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com