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Bandwidth Selection for Prediction in Regression [PDF]
Proceedings, 2019 There exist many different methods to choose the bandwidth in kernel regression. If, however, the target is regression based prediction for samples or populations with potentially different distributions, then the existing methods can easily be ...
Inés Barbeito +2 more
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New Bandwidth Selection for Kernel Quantile Estimators [PDF]
Journal of Probability and Statistics, 2012 We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a ...
Ali Al-Kenani, Keming Yu
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Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data
Stats, 2022 Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse.
Sudaraka Tholkage +2 more
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Energies, 2022 This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE).
Juseung Choi +2 more
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Edge-Preserving Denoising of Image Sequences
Entropy, 2021 To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time.
Fan Yi, Peihua Qiu
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Bootstrap Bandwidth Selection Using anh‐Dependent Pilot Bandwidth [PDF]
Scandinavian Journal of Statistics, 2008 Abstract. The problem of choosing the bandwidthhfor kernel density estimation is considered. All the plug‐in‐type bandwidth selection methods require the use of a pilot bandwidthg. The usual way to make anh‐dependent choice ofgis by obtaining their asymptotic expressions separately and solving the two equations.
Chacón, José E. +2 more
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lpdensity: Local Polynomial Density Estimation and Inference
Journal of Statistical Software, 2022 Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known ...
Matias D. Cattaneo +2 more
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Bandwidth selection in pre-smoothed particle filters [PDF]
Statistics and Computing, 2012 For the purpose of maximum likelihood estimation of static parameters, we apply a kernel smoother to the particles in the standard SIR filter for non-linear state space models with additive Gaussian observation noise. This reduces the Monte Carlo error in the estimates of both the posterior density of the states and the marginal density of the ...
Kleppe, Tore Selland, Skaug, Hans J.
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Boundary Kernels for Distribution Function Estimation
Revstat Statistical Journal, 2013 Boundary effects for kernel estimators of curves with compact supports are well known in regression and density estimation frameworks. In this paper we address the use of boundary kernels for distribution function estimation.
Carlos Tenreiro
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IEEE Access, 2023 Currently, streaming communications are widespread through YouTube and other media. Streaming communication focuses on real-time communication and requires flow admission control to ensure communication quality.
Sumiko Miyata, Taichi Kojima
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