Results 61 to 70 of about 231,500 (249)
IEEE Access, 2019 In this paper, a nonparametric spatial-temporal self-exciting point process is proposed to model clustering features in emergency calls. Gaussian kernel density functions are considered.
Chenlong Li, Zhanjie Song, Xu Wang
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Nonparametric IV estimation of shape-invariant Engel curves [PDF]
, 2003 This paper concerns the identification and estimation of a shape-invariant Engel curve system with endogenous total expenditure. The shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of Engel
Blundell, R., Chen, X., Kristensen, D.
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Arthritis Care &Research, Accepted Article.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
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Nonparametric Efficiency Estimation in Stochastic Environments (II) [PDF]
We consider the issues of noise-to-signal estimation, finite sample performance andhypothesis testing for the nonparametric efficiency estimation technique proposed inCherchye, L., T. Kuosmanen and G. T. Post (2001) 'Nonparametric efficiencyestimation in
Cherchye, L., Post, G.T.
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Advanced Engineering Materials, EarlyView.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
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Nonparametric Estimation of the Tail-Dependence Coefficient
Revstat Statistical Journal, 2013 A common measure of tail dependence is the so-called tail-dependence coefficient. We present a nonparametric estimator of the tail-dependence coefficient and prove its strong consistency and asymptotic normality in the case of known marginal ...
Marta Ferreira
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Adaptive orthogonal series estimation in additive stochastic regression models [PDF]
In this paper, we consider additive stochastic nonparametric regression models. By approximating the nonparametric components by a class of orthogonal series and using a generalized cross-validation criterion, an adaptive and simultaneous estimation ...
Howell Tong, Jiti Gao, Rodney C Wolff
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.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
Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric
Econometrics, 2019 It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known.
Kyoo il Kim
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Matrix Viscoelasticity Regulates Dendritic Cell Migration and Immune Priming
Advanced Materials, EarlyView.Matrix viscoelasticity is a key mechanical feature of the tumor microenvironment but remains poorly understood in immune regulation. Here, we develop a tunable collagen platform to decouple viscoelasticity from stiffness and show that slow‐relaxing matrices constrain dendritic cell migration by limiting actomyosin‐driven matrix remodeling, thereby ...
Wei‐Hung Jung +6 more
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