Results 11 to 20 of about 406,123 (263)
Efficient Robbins–Monro procedure for multivariate binary data
Statistical Theory and Related Fields, 2018 This paper considers the problem of jointly estimating marginal quantiles of a multivariate distribution. A sufficient condition for an estimator that converges in probability under a multivariate version of Robbins–Monro procedure is provided.
Cui Xiong, Jin Xu
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Constant Information Design for Binary Response Data
Sultan Qaboos University Journal for Science, 1998 A major problem is designing experiments when the assumed model is nonlinear, is the dependence of the designs on the values of the unknown parameters we consider in this article designs for binary data and generalize the constant information criterion ...
K.A. Abdelbasit
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Endogeneity in Semiparametric Binary Response Models [PDF]
Review of Economic Studies, 2004 Summary: This paper develops and implements semiparametric methods for estimating binary response (binary choice) models with continuous endogenous regressors. It extends existing results on semiparametric estimation in single-index binary response models to the case of endogenous regressors.
Richard W. Blundell, James L. Powell
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General Psychiatry, 2023 Although logistic regression is the most popular for modelling regression relationships with binary responses, many find relative risk (RR), or risk ratio, easier to interpret and prefer to use this measure of risk in regression analysis.
Xin M Tu +5 more
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When binary and continuous responses disagree [PDF]
Annals of the Rheumatic Diseases, 2020 In the observational TOCERRA study by Lauper et al ,1 the authors showed that tocilizumab (TOC; either as monotherapy or combination therapy) had superior drug retention than tumour necrosis factor inhibitors (TNFi; as monotherapy or combination therapy), in patients with rheumatoid arthritis with prior exposure to at least one biologic disease ...
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Genetic evaluation for multiple binary responses [PDF]
Genetics Selection Evolution, 1986 A method of genetic evaluation for multiple binary responses is presented. An underlying multivariate normal distribution is rendered discrete, in m dimensions, via a set of m fixed thresholds. There are 2m categories of response and the probability of response in a given category is modeled with an m-dimensional multivariate normal integral.
Hoschele, I. +3 more
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D-Optimal Designs for Binary and Weighted Linear Regression Models: One Design Variable
Mathematics, 2023 D-optimality is a well-known concept in experimental design that seeks to select an optimal set of design points to estimate the unknown parameters of a statistical model with a minimum variance.
Necla Gündüz, Bernard Torsney
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PLoS Biology, 2012 Erythropoietin (Epo)-induced Stat5 phosphorylation (p-Stat5) is essential for both basal erythropoiesis and for its acceleration during hypoxic stress. A key challenge lies in understanding how Stat5 signaling elicits distinct functions during basal and ...
Ermelinda Porpiglia +4 more
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Annealing Response of a Cold-Rolled Binary Al–10Mg Alloy
Metals, 2019 The effect of annealing temperature on microstructure and mechanical properties of a cold-rolled Al−10Mg alloy has been investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and tensile
Lei Feng +3 more
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RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach
Mathematics, 2021 A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows ...
Jessica Pesantez-Narvaez +2 more
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