Results 61 to 70 of about 281,081 (285)
Confidence sets for nonparametric wavelet regression
We construct nonparametric confidence sets for regression functions using wavelets that are uniform over Besov balls. We consider both thresholding and modulation estimators for the wavelet coefficients.
Genovese, Christopher R. +1 more
core +2 more sources
Objective To investigate the association between rheumatoid arthritis (RA) and coronary artery calcium (CAC) prevalence, incidence, and progression over four years in adults without prior cardiovascular disease. Methods A case‐cohort study within ELSA‐Brasil included 585 participants (86 RA, 499 controls). Longitudinal analyses were restricted to those
Patrícia Fonseca Estrada +7 more
wiley +1 more source
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Robust nonparametric estimation via wavelet median regression [PDF]
In this paper we develop a nonparametric regression method that is simultaneously adaptive over a wide range of function classes for the regression function and robust over a large collection of error distributions, including those that are heavy-tailed,
Brown, Lawrence D. +2 more
core +3 more sources
A nonparametric approach for quantile regression [PDF]
Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles.
Huang, Mei Ling, Nguyen, Christine
openaire +2 more sources
Comparison of Nonparametric Path Analysis and Biresponse Regression using Truncated Spline Approach
Nonparametric path analysis and biresponse nonparametric regression are two flexible statistical approaches to analyze the relationship between variables without assuming a certain form of relationship.
Laila Nur Azizah +4 more
doaj +1 more source
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Endogeneity in nonparametric and semiparametric regression models [PDF]
This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors. We list a number of different generalizations of the linear structural equation model, and discuss how two common ...
Blundell, R., Powell, J.L.
core +1 more source
3D Soft Hydrogels Induce Human Mesenchymal Stem Cells “Deep” Quiescence
Three‐dimensional soft hydrogels mimicking the bone marrow niche induce deep quiescence in human mesenchymal stem cells. Unlike 2D culture, 3D matrices halt proliferation, regulate cell‐cycle and quiescence markers, and downregulate mTORC1 signaling, preserving stem cell phenotype and therapeutic potential ex vivo.
David Boaventura Gomes +11 more
wiley +1 more source
Nonparametric Instrumental Regression [PDF]
Summary: The focus of this paper is the nonparametric estimation of an instrumental regression function \(\varphi\) defined by conditional moment restrictions that stem from a structural econometric model \(E[Y - \varphi (Z)|W]=0\), and involve endogenous variables \(Y\) and \(Z\) and instruments \(W\).
Darolles, Serge +3 more
openaire +4 more sources

