… In Section 5 we investigate the finite sample performance of the adaptive estimator through
… ∗ is the geometric mean of n1 and n2. Our rationale for this definition of the effective sample …

… Using n = 200 and n = 2000 observations per sample, we examined the performance of
our pseudodata estimator with eight pseudodata rules, and the reflection and boundary …

… to kernel density estimation (KDE) and recent advances regarding confidence bands and
geometric… Then, we apply KDE to the bootstrap sample, resulting in a bootstrap KDE . When we …

… higher order kernels, we find a formula for a kernel density … This assumption, which we
call the geometric prior, is a … We define the “cut-and-normalize” method to discard samples …

… mont For each simulated sampling regime we calculated t number of days sampled. We fitted
as … Our results suggest that the kernel method is more efficient and better behaved than the …

… sampling usually has a wide shoulder near x = 0 and a flat valley near s = 0, as assumed by
(2.1). Applying the kernel estimator to the extended sample … to make the geometric mean of …

… correction based on geometric probability equations. The approach uses the fact that when
particles of a particular grade are sectioned there are nine geometric … mounted samples for …

… kernel regression, a well-known technique in machine learning. Specifically, we estimate
the OPC patterns based on the geometric … concentric circular sampling method to sample its …

… , wheref(X) is the pilot fixed kernel density estimate, g is the geometric mean of the pilot den-
… Kernel-based estimates from small samples will be poor at identifying fine structure and will …

… out using a smooth reconstruction kernel applied on the … of the number of down-sampled
trajectory points Nα on the … algorithm and provide a geometry correction for flexible trajectories …