Results 51 to 60 of about 257,461 (188)

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

Acta Universitatis Lodziensis. Folia Oeconomica, 2017
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.
Aleksandra Katarzyna Baszczyńska
doaj   +1 more source

Nonparametric methods for volatility density estimation

, 2009
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process.
Spreij, Peter, van Es, Bert, van Zanten, Harry   +2 more
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A Novel Flexible Kernel Density Estimator for Multimodal Probability Density Functions

CAAI Transactions on Intelligence Technology
Estimating probability density functions (PDFs) is critical in data analysis, particularly for complex multimodal distributions. traditional kernel density estimator (KDE) methods often face challenges in accurately capturing multimodal structures due to
Jia‐Qi Chen, Yu‐Lin He, Ying‐Chao Cheng, Philippe Fournier‐Viger, Ponnuthurai Nagaratnam Suganthan, Joshua Zhexue Huang   +5 more
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Shape constrained kernel density estimation [PDF]

In this paper, a method for estimating monotone, convex and log-concave densities is proposed. The estimation procedure consists of an unconstrained kernel estimator which is modi?ed in a second step with respect to the desired shape constraint by using ...
Birke, Melanie
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Heuristic Kernel Density Estimator for Modal‑Proximity Data

Shuju Caiji Yu Chuli
Different from the classical probability density estimator construction strategies based on the Parzen window method， we propose a heuristic kernel density estimator （HKDE） based on nearest neighbor error measurement function， to improve the accuracy of ...
HE Yulin, CHEN Chunjia, HUANG Zhexue, LI Junjie, FOURNIER‑VIGER Philippe   +4 more
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Estimating a Distribution Function at the Boundary

Austrian Journal of Statistics, 2020
Estimation of distribution functions has many real-world applications. We study kernel estimation of a distribution function when the density function has compact support. We show that, for densities taking value zero at the endpoints of the support, the
Shunpu Zhang, Zhong Li, Zhiying Zhang
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Enhancing Broiler Weight Estimation through Gaussian Kernel Density Estimation Modeling

Agriculture
The management of individual weights in broiler farming is not only crucial for increasing farm income but also directly linked to the revenue growth of integrated broiler companies, necessitating prompt resolution.
Yumi Oh, Peng Lyu, Sunwoo Ko, Jeongik Min, Juwhan Song   +4 more
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A Two-Stage Plug-In Bandwidth Selection and Its Implementation in Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation [PDF]

The performance of a kernel HAC estimator depends on the accuracy of the estimation of the normalized curvature, an unknown quantity in the optimal bandwidth represented as the spectral density and its derivative.
Hirukawa Masayuki
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Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach

Agriculture
Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies.
Lili Jiang, Yunfei Wang, Chong Wu, Haibin Wu   +3 more
doaj   +1 more source

Nonstationary Density Estimation and Kernel Autoregression [PDF]

An asymptotic theory is developed for the kernel density estimate of a random walk and the kernel regression estimator of a nonstationary first order autoregression.
Joon Y. Park, Peter C.B. Phillips
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