Results 141 to 150 of about 55,553 (309)
Conditional density estimation: an application to the Ecuadorian manufacturing sector [PDF]
This note applies conditional density estimation as a visual method to present results. The proposed method is illustrated by application to a firm-level manufacturing data set from Ecuador in 2002.Density ...
Kim Huynh, David Jacho-Chavez
core
Advanced Science, EarlyView.A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei +7 more
wiley +1 more source
Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation. [PDF]
Sensors (Basel), 2023 Wang J +5 more
europepmc +1 more source
Differentially Private Kernel Density Estimation
CoRRv2: Appendix added.
Erzhi Liu +4 more
openaire +2 more sources
Nonparametric Estimation and Symmetry Tests for Conditional Density Functions. [PDF]
We suggest two new methods for conditional density estimation. The first is based on locally fitting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation.
Hyndman, R.J., Yao, Q.
core
Stress‐to‐Light Conversion in an Earth‐Abundant Oxide Semiconductor
Advanced Science, EarlyView.Stress‐to‐light conversion in solids represents a unique photonic functionality, yet it has never been realized in a chemically simple and sustainable material. Here, we show that sustainable semiconductor ZnO exhibits strong near‐infrared (NIR) luminescence under elastic stress when defect‐engineered to stabilize the p‐type state.
Tomoki Uchiyama +7 more
wiley +1 more source
Enhancing Broiler Weight Prediction via Preprocessed Kernel Density Estimation
AgricultureAccurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation (CV)
Sangmin Yoo, Yumi Oh, Juwhan Song
doaj +1 more source
A close contact identification algorithm using kernel density estimation for the ship passenger health. [PDF]
J King Saud Univ Comput Inf Sci, 2023 Lin Q, Son J.
europepmc +1 more source
Kernel Density Estimated Linear Regression
The International FLAIRS Conference ProceedingsRegression analysis is a cornerstone of predictive modeling, with linear regression and kernel regression standing as two of its most prominent paradigms. However, each approach has inherent limitations: linear regression is highly susceptible to outliers in noisy and unevenly distributed datasets, while kernel regression often suffers from overfitting.
Roshan Kalpavruksha +3 more
openaire +2 more sources
Advanced Science, EarlyView.SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang +8 more
wiley +1 more source