Kernel density estimation bootstrap estimates results
Bootstrap CI and test statistics for kernel density estimates using Stata
... Hence, the bootstrap provides asymptotic refinements for hypothesis tests and confidence intervals based on a kernel nonparametric density estimator (when the bandwidth h n. converges to[r] ...
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Kernel density estimation on the torus
... proposed density derivatives estimator and, finally, we discuss its minimax ...of kernel and bandwidth, is replaced by the single step of selecting the Fourier coefficients of the optimal ...of ...
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Kernel deconvolution density estimation
... joint density of the “usual” or long-run average of serum iPTH (intact parathyroid hormone) and 25(OH)D (25-hydroxy vitamin D), both of which are associated with bone ...noisy estimates of their long-run ...
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Robust Kernel Density Estimation
... reproducing kernel, only that the functions Φ(x) = k σ ( · , x) belong to a Hilbert ...the results presented below hold in this more general ...the kernel, leading to efficient ...
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Parallel Computing of Kernel Density Estimates with MPI
... Abstract. Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic density ...time. Results of using reference implemen- tation based on Message Passing ...
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Do-Validation for Kernel Density Estimation
... two terms. All bandwidths have the same asymptotic first component. The second component varies with the different methods. While it is quite large for the cross- validation method, it is less than one third of this value ...
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Shape constrained kernel density estimation
... resulting estimates are presented in section 2 together with some asymptotic ...convex density estimate still holds if the true density is only two times continuously ...
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Optimal kernel selection for density estimation
... selected estimates by the optimal penalty 2K a (0)/(nh) for the different values of a and on Figure 2 one sees the evolution of the different penalized criteria as a function of ...
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Improving bias in kernel density estimation
... estimated density. Unfortunately, estimation results with T a K were strictly better than with conventional kernels only for kernel orders q = 6, 8, 10, ...transformed kernel with m = ...
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Kernel Density Estimation for Heaped Data
... for kernel density estimation for heaped data was ...non-parametric density estimates and gives additional insights into the rounding ...
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Robust Kernel Density Function Estimation
... The research also develops two new approaches for outlier and potential outlier detection in unimodal and multimodal distributions. The distance of observations from the center of data set is incorporated in the ...
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Kernel Density Estimation for Dynamical Systems
... these results has received funding from the European Research Coun- cil under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC AdG A-DATADRIVE-B ...
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Shape constrained kernel density estimation
... resulting estimates are presented in section 2 together with some asymptotic ...convex density estimate still holds if the true density is only two times continuously ...
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Validating protein structure using kernel density estimates
... bioinformatics. Kernel density estimation is a well-known nonparametric method which is often used for exploratory data ...conditional density estimates, a method is proposed which ...
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Neural Representations and Decoding with Optimized Kernel Density Estimates
... Impact of the ensemble size on the decoding accuracy: Figure 5.3, top panel illustrates misclassifi- cation error curves as a function of the subset size of neurons which was employed to construct the LDA classifier for ...
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Validating protein structure using kernel density estimates
... bioinformatics. Kernel density estimation is a well-known nonparametric method which is often used for exploratory data ...conditional density estimates, a method is proposed which ...
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Methods for Shape-Constrained Kernel Density Estimation
... convey the same general structure present in the pilot estimate, but with increasing degrees of smoothing in their outer contour lines. These bivariate runs provide another opportunity to observe the convergence be- ...
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Kernel density estimation with doubly truncated data
... NPMLE. Bootstrap methods to approximate the finite sample distribution of the NPMLE with doubly truncated data were explored in ...knowledge, estimation of a density function observed under random ...
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Development and application of traffic accident density estimation models using kernel density estimation
... accident density estimation explained by GIS data can enable the determination of dangerous areas objectively and easily, indi- cating where area-wide traffic calming can be implemented ...accident ...
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Using conditional kernel density estimation for wind power density forecasting
... Abstract Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of ...
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