Results 141 to 150 of about 128,596 (295)
Nonparametric Econometrics: The np Package [PDF]
We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians.
Tristen Hayfield, Jeffrey S. Racine
core +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
Advanced Intelligent Discovery, EarlyView.As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
A Bistochastic Nonparametric Estimator [PDF]
We explore the relevance of adopting a bistochastic nonparametric estimator. This estimator has two main implications. First, the estimator reduces variability according to the robust criterion of second-order stochastic (and Lorenz) dominance. This is a
Rafael Salas, Juan Gabriel Rodríguez
core
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
POINTWISE UNIVERSAL CONSISTENCY OF NONPARAMETRIC LINEAR ESTIMATORS [PDF]
This paper presents sufficient conditions for pointwise universal consistency of nonparametric delta estimators. We show the applicability of these conditions for some classes of nonparametric estimators.
Jose M. Vidal-Sanz
core
AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes
Advanced Intelligent Discovery, EarlyView.To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song +6 more
wiley +1 more source
Nonparametric Priors for Vectors of Survival Functions [PDF]
The paper proposes a new nonparametric prior for two–dimensional vectors of survival functions (S1, S2). The definition we introduce is based on the notion of L´evy copula and it will be used to model, in a nonparametric Bayesian framework, two–sample ...
Antonio Lijoi, Ilenia Epifani
core +3 more sources
Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals [PDF]
This paper studies nonparametric estimation of conditional moment models in which the generalized residual functions can be nonsmooth in the unknown functions of endogenous variables. This is a nonparametric nonlinear instrumental variables (IV) problem.
Demian Pouzo, Xiaohong Chen
core
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Advanced Intelligent Discovery, EarlyView.Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
LogConcDEAD: An R Package for Maximum Likelihood Estimation of a Multivariate Log-Concave Density
Journal of Statistical Software, 2008 In this article we introduce the R package LogConcDEAD (Log-concave density estimation in arbitrary dimensions). Its main function is to compute the nonparametric maximum likelihood estimator of a log-concave density.
Madeleine Cule +2 more
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