Results 81 to 90 of about 55,553 (309)

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
doaj   +1 more source

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

Alternating kernel and mixture density estimates

Computational Statistics & Data Analysis, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Priebe, C. E., Marchette, D. J.
openaire   +2 more sources

A Bayesian approach to parameter estimation for kernel density estimation via transformations [PDF]

In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations.
David Pitt, Xueyuan Wu, Qing Liu, Xibin Zhang   +3 more
core  

Maximum kernel likelihood estimation [PDF]

, 2008
We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
core  

Orthogonal-least-squares regression: A unified approach for data modelling [PDF]

, 2008
A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test
Harris, C. J., X. Hong, B.L. Luk, Chen, Sheng, C.J. Harris, Chen, S., Luk, Bing L., Hong, Xia, B. L. Luk, S. Chen, Luk, B. L., Hong, X., C. J. Harris, Harris, Chris   +13 more
core   +1 more source

Buckling‐Driven Linearity in Flexible Capacitive Pressure Sensors Based on a Three‐Dimensional Dual‐Pore Polyurethane Structure

Advanced Materials Technologies, EarlyView.
A flexible pressure sensor utilizing a 3D dual‐pore polyurethane structure is developed to overcome the intrinsic trade‐off between sensitivity and linearity. By inducing sequential buckling through distinct pore sizes and shapes, the device achieves highly linear and sensitive responses across a wide pressure range.
Jae Yeong Jang, Jaemin Choi, Young Jung
wiley   +1 more source

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
doaj   +1 more source

Mobius-like mappings and their use in kernel density estimation [PDF]

, 2003
It is well known that the manipulation of sample data by means of a parametric function can improve the performance of kernel density estimation. This article proposes a two-parameter Mobius-like function to map sample data drawn from a semi-infinite ...
Clements, Adam E., Clements, A.E., Lindsay, K.A., Lindsay, Kenneth, Hurn, A.S., Hurn, Stanley   +5 more
core   +1 more source

Bayesian‐Optimized Dual‐Mode Luminescence‐Based Optical Thermometry Enabled by Negative Thermal Expansion in Lu2(MoO4)3:Yb–Er–Tm Phosphor

Advanced Optical Materials, EarlyView.
A Bayesian optimization framework identifies the ideal composition for Lu2(MoO4)3:Yb–Er–Tm phosphors with minimal experimental trials. By leveraging the host's negative thermal expansion, the material achieves remarkable thermal quenching compensation.
Reiko Furukawa, Takuya Hasegawa, Tong Fang, Saneyuki Ohno, Tomoyo Goto, Yasushi Sato, Ayahisa Okawa, Shu Yin   +7 more
wiley   +1 more source