Bayesian and non-bayesian approaches for estimating the Epanechnikov-Weibull distribution with applications in engineering and electronics data. [PDF]
Sci RepTaher TS +4 more
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Decision-Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets. [PDF]
Entropy (Basel)Dellaporta C, O'Hara P, Damoulas T.
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Evaluating the coupling coordination between industrial chains and innovation chains in the construction industry of Western China from a production-manufacturing perspective. [PDF]
Sci RepXu J, Zhang Y, Chen Z.
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Cluster analysis for longitudinal data and its application in the detection of adiposity trajectories. [PDF]
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Nonparametric density estimation using kernels with variable size windows
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The Kernel density estimation of nonparametric model
2009 Chinese Control and Decision Conference, 2009Four nonparametric estimates of a density function are investigated. Two model estimates are defined from a global kernel estimate, while the other two are defined from a global kernel estimate of the first derivative of the density function. We show that each of these model estimates attains the same rate of convergence as the usual sample model. Then,
Jifu Nong
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Probability Density Estimation Based on Nonparametric Local Kernel Regression
Lecture Notes in Computer Science, 2010In this research, a local kernel regression method was proposed to improve the computational efficiency after analyzing the kernel weights of the nonparametric kernel regression Based on the correlation between the distribution function and the probability density function, together with the nonparametric local kernel regression we developed a new ...
Min Han
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Deconvolution boundary kernel method in nonparametric density estimation
Journal of Statistical Planning and Inference, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rohana J Karunamuni, Shunpu Zhang
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Nonparametric Probabilistic Unbalanced Power Flow With Adaptive Kernel Density Estimator
IEEE Transactions on Smart Grid, 2019This paper presents a nonparametric algorithm to estimate probability density functions of power flow outputs in unbalanced distribution systems. The proposed algorithm is based on an adaptive kernel density estimation. As the main advantage, the proposed algorithm is: 1) applicable for power flow problems with unknown classes of probability ...
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On nonparametric kernel density estimates
Biometrika, 1990SUMMARY The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error.
M. Samiuddin, G. M. El-Sayyad
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