Bandwidth Selection in Geographically Weighted Regression Models via Information Complexity Criteria
Journal of Mathematics, 2022 The geographically weighted regression (GWR) model is a local spatial regression technique used to determine and map spatial variations in the relationships between variables.
Tuba Koç
doaj +2 more sources
X-Entropy: A Parallelized Kernel Density Estimator with Automated Bandwidth Selection to Calculate Entropy. [PDF]
J Chem Inf Model, 2021 X-Entropy is a Python package used to calculate the entropy of a given distribution, in this case, based on the distribution of dihedral angles. The dihedral entropy facilitates an alignment-independent measure of local protein flexibility.
Kraml J +4 more
europepmc +2 more sources
Bandwidth selection for nonparametric modal regression [PDF]
Communications in Statistics - Simulation and Computation, 2018 In the context of estimating local modes of a conditional density based on kernel density estimators, we show that existing bandwidth selection methods developed for kernel density estimation are unsuitable for mode estimation. We propose two methods to select bandwidths tailored for mode estimation in the regression setting.
Zhou, Haiming, Huang, Xianzheng
openaire +4 more sources
Gaussian bandwidth selection for manifold learning and classification. [PDF]
Data Min Knowl Discov, 2020 Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks.
Lindenbaum O +3 more
europepmc +2 more sources
Optimal bandwidth selection for semi-recursive kernel regression estimators [PDF]
, 2016 In this paper we propose an automatic selection of the bandwidth of the semi-recursive kernel estimators of a regression function defined by the stochastic approximation algorithm.
Slaoui, Yousri
core +4 more sources
Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions [PDF]
Annals of the Institute of Statistical Mathematics, 2022 We introduce a new fully non-parametric two-step adaptive bandwidth selection method for kernel estimators of spatial point process intensity functions based on the Campbell–Mecke formula and Abramson’s square root law.
M. N. M. van Lieshout
openalex +2 more sources
Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspective [PDF]
Global Communications Conference, 2022 Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning on clients with protecting user information ...
Yun Ji +5 more
openalex +3 more sources
A Plug‐in Bandwidth Selection Procedure for Long‐Run Covariance Estimation with Stationary Functional Time Series [PDF]
, 2016 In several arenas of application, it is becoming increasingly common to consider time series of curves or functions. Many inferential procedures employed in the analysis of such data involve the long‐run covariance function or operator, which is ...
Gregory Rice, Han Lin Shang
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Bandwidth selection for nonparametric regression with errors-in-variables
Econometric Reviews, 2023 We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first
Hao Dong, Taisuke Otsu, L. Taylor
semanticscholar +1 more source