Results 11 to 20 of about 12,339 (203)
Causal Isotonic Regression. [PDF]
J R Stat Soc Series B Stat Methodol, 2020 SummaryIn observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose–response curve can be recovered by using the G-computation formula. Most classical methods for estimating such curves when the exposure is continuous rely on restrictive parametric ...
Westling T, Gilbert P, Carone M.
europepmc +5 more sources
Isotonic Distributional Regression [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2021 AbstractIsotonic distributional regression (IDR) is a powerful non-parametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity ...
Henzi, Alexander +2 more
openaire +4 more sources
Advances in Neural Information Processing Systems 35, 2022 Neural Information Processing Systems (NeurIPS ...
Badih Ghazi +3 more
openaire +3 more sources
Iterative isotonic regression [PDF]
ESAIM: Probability and Statistics, 2015 This article introduces a new nonparametric method for estimating a univariate regression function of bounded variation. The method exploits the Jordan decomposition which states that a function of bounded variation can be decomposed as the sum of a non-decreasing function and a non-increasing function. This suggests combining the backfitting algorithm
Guyader, Arnaud +3 more
openaire +5 more sources
CoRR, 2016 We consider the online version of the isotonic regression problem. Given a set of linearly ordered points (e.g., on the real line), the learner must predict labels sequentially at adversarially chosen positions and is evaluated by her total squared loss compared against the best isotonic (non-decreasing) function in hindsight.
W.T. Kotlowski (Wojciech) +2 more
openaire +3 more sources
Isotonic Regression under Lipschitz Constraint. [PDF]
J Optim Theory Appl, 2009 The pool adjacent violators (PAV) algorithm is an efficient technique for the class of isotonic regression problems with complete ordering. The algorithm yields a stepwise isotonic estimate which approximates the function and assigns maximum likelihood to the data.
Yeganova L, Wilbur WJ.
europepmc +4 more sources
Isotonic regression discontinuity designs [PDF]
Journal of Econometrics, 2019 This paper studies the estimation and inference for the isotonic regression at the boundary point, an object that is particularly interesting and required in the analysis of monotone regression discontinuity designs. We show that the isotonic regression is inconsistent in this setting and derive the asymptotic distributions of boundary corrected ...
Babii, Andrii, Kumar, Rohit
openaire +3 more sources
meta.shrinkage: An R Package for Meta-Analyses for Simultaneously Estimating Individual Means
Algorithms, 2022 Meta-analysis is an indispensable tool for synthesizing statistical results obtained from individual studies. Recently, non-Bayesian estimators for individual means were proposed by applying three methods: the James–Stein (JS) shrinkage estimator ...
Nanami Taketomi +3 more
doaj +1 more source
The role of the host—Neutrophil biology
Periodontology 2000, EarlyView., 2023 Abstract Neutrophilic polymorphonuclear leukocytes (neutrophils) are myeloid cells packed with lysosomal granules (hence also called granulocytes) that contain a formidable antimicrobial arsenal. They are terminally differentiated cells that play a critical role in acute and chronic inflammation, as well as in the resolution of inflammation and wound ...
Iain L. C. Chapple +4 more
wiley +1 more source
Bayesian isotonic density regression [PDF]
Biometrika, 2011 Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications.
Lianming Wang, David B. Dunson
openaire +3 more sources