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Informed Injury Prediction in Elite Football: Decision Theory meets Machine Learning

Huth M, Canal-Simón B, Ferrer E, Rodas G, Yanguas X, Hasenauer J, González JR.   +6 more
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Monotone Regrouping, Regression, and Simpson's Paradox [PDF]

The American Statistician, 2003
This article shows in a general setup that if data Y are grouped by a covariate X in a certain way, then under a condition of monotone regression of Y on X, a Simpson's type paradox is natural rather than surprising. This model was motivated by an observation on recent SAT data which are presented.
Rinott Y., Tam M.
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Primal-Dual Monotone Kernel Regression

Neural Processing Letters, 2005
This paper considers the estimation of monotone nonlinear regression functions based on Support Vector Machines (SVMs), Least Squares SVMs (LS-SVMs) and other kernel machines. It illustrates how to employ the primal-dual optimization framework characterizing LS-SVMs in order to derive a globally optimal one-stage estimator for monotone regression. As a
K. Pelckmans, M. Espinoza, J. De Brabanter, J. A. K. Suykens, B. De Moor   +4 more
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A Simple Method for Pairwise Monotone Regression

Psychometrika, 1975
A simple method of monotone regression is described based on the principle of minimizing pairwise departures from monotonicity.
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About monotone regression quantiles

Statistics & Probability Letters, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Poiraud-Casanova, Sandrine, Thomas-Agnan, Christine   +1 more
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Windowed locally monotonic regression

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991
Lomotonicity, the largest degree of local monotonicity that a signal has, is proposed as an appropriate measure of smoothness when studying smoothers such as the median filter. Locally monotonic regression (LMR) optimally solves the problem of smoothing a signal to a specified minimal degree of lomotonicity, but it often requires an excess of ...
A. Restrepo, A.C. Bovik
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Testing Monotonicity of Regression

Journal of Computational and Graphical Statistics, 1998
Abstract This article provides a test of monotonicity of a regression function. The test is based on the size of a “critical” bandwidth, the amount of smoothing necessary to force a nonparametric regression estimate to be monotone. It is analogous to Silverman's test of multimodality in density estimation.
A. W. Bowman, M. C. Jones, I. Gijbels
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