scholarly journals Smoothing parameter values in automatic choice procedure and in acceptable interval in the kernel density estimation

2018 ◽  
Vol 68 (3) ◽  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Smoothing Parameter ◽  
Choice Procedure ◽  
Automatic Choice ◽  
Parameter Values

scholarly journals One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation

2018 ◽  
Vol 6 (332) ◽  
pp. 73-86
Author(s):  
Aleksandra Katarzyna Baszczyńska
Keyword(s):  
Density Function ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Ad Hoc ◽  
Kernel Density ◽  
Kernel Estimator ◽  
Smoothing Parameter ◽  
Smoothing Parameter Selection ◽  
Starting Point ◽  
Parameter Values

Ad hoc methods in the choice of smoothing parameter in kernel density estimation, al­though often used in practice due to their simplicity and hence the calculated efficiency, are char­acterized by quite big error. The value of the smoothing parameter chosen by Silverman method is close to optimal value only when the density function in population is the normal one. Therefore, this method is mainly used at the initial stage of determining a kernel estimator and can be used only as a starting point for further exploration of the smoothing parameter value. This paper pre­sents ad hoc methods for determining the smoothing parameter. Moreover, the interval of smooth­ing parameter values is proposed in the estimation of kernel density function. Basing on the results of simulation studies, the properties of smoothing parameter selection methods are discussed.

scholarly journals Speaking Stata: Density Probability Plots

2005 ◽  
Vol 5 (2) ◽  
pp. 259-273 ◽  
Author(s):  
Nicholas J. Cox
Keyword(s):  
Order Statistics ◽  
Density Function ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Real Data ◽  
Continuous Variable ◽  
Quantile Plots ◽  
Probability Plots ◽  
Parameter Values

Density probability plots show two guesses at the density function of a continuous variable, given a data sample. The first guess is the density function of a specified distribution (e.g., normal, exponential, gamma, etc.) with appropriate parameter values plugged in. The second guess is the same density function evaluated at quantiles corresponding to plotting positions associated with the sample's order statistics. If the specified distribution fits well, the two guesses will be close. Such plots, suggested by Jones and Daly in 1995, are explained and discussed with examples from simulated and real data. Comparisons are made with histograms, kernel density estimation, and quantile–quantile plots.

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

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