The adaptive kernel density estimate is given by fˆ(x) = 1 n i=1 wi n i=1 wi K hi x − xi hi (1) where the xi's are the data points (associated with weights wi), K is a kernel function, and hi = h×λi. (Compare with [R] kdensity.) The local bandwidth factors ...
Methods In this paper, adaptive kernel density estimation was used to estimate probability densities for distribution entropy (DistEn), fuzzy DistEn (FDistEn), complex-valued DistEn (CVFDistEn) and complex-valued FDistEn (CVFDistEn). Totally six hierarchical entropies were then raised, to ...
The kernel size h is the most important characteristic of the Parzen density estimate (Raudys, 1991; Silverman, 1978). One can compute the ideal or optimal value of h by minimizing the mean-square errorMSE{f̂h(x)}=E{[f̂h(x)−f(x)]2}between the true and estimated densities,...
The present study uses the adaptive kernel density to estimate a spatial risk surface based on non-aggregated data for identifying areas of elevated cancer incidences. We tested the applicability of this new method for use as an exploratory screening procedure for elevated cancer risks in a ...
method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving ... P Xin,Y Liu,N Yang,... - 《全球能源互联网:英文版》 被引量: 0发表: 2020年 Method to estimate sag frequency in doubly fed induction ...
Its application promises to predict the flutter boundary of airfoil under complex flight conditions, and estimate the airfoil’s flutter instability probability. Notably, in the process of estimating the instability probability based on a generalized stochastic basin, it is crucial to determine the ...
A tool for user choice of the local bandwidth function for a kernel density estimate is developed using KDE, a graphical object-oriented package for intera... JS Marron,F Udina 被引量: 0发表: 1995年 Adaptive bandwidth choice for infinite order spectral density estimates A tool for user choice...
B. W. Silverman, ‘‘Weak and strong uniform consistency of the kernel estimate of a density and its derivatives,’’ The Annals of Statistics, 177–184 (1978). R. Singh, ‘‘Mean squared errors of estimates of a density and its derivatives,’’ Biometrika 66 (1), 177–180 (1979)....
Bayesian classifiers based on kernel density estimation: flexible classifiers Int. J. Approx. Reason. (2009) M. Sousa de Lima et al. A Bayesian method to estimate the optimal bandwidth for multivariate kernel estimator J. Nonparametric Stat. (2011) A.Z. Zambom et al. A review of kernel de...
The smoothness of the density estimate is determined by the form of the kernel, in particular its bandwidth. Wider kernels produce smoother density estimates, whereas narrow kernels produce bumpier estimates. For each rotamer, r, of a given residue type, we determine a probability density estimate...