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Primal-Dual Monotone Kernel Regression
Neural Processing Letters, 2005This 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
Kristiaan Pelckmans +4 more
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Inverse boosting for monotone regression functions
Computational Statistics & Data Analysis, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuwon Kim, Ja-Yong Koo
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On the statistical optimality of locally monotonic regression
IEEE Transactions on Signal Processing, 1994Locally monotonic regression is a recently proposed technique for the deterministic smoothing of finite-length discrete signals under the smoothing criterion of local monotonicity. Locally monotonic regression falls within a general framework for the processing of signals that may be characterized in three ways: regressions are given by projections ...
Alfredo Restrepo Palacios, Alan C. Bovik
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Non-Euclidean locally monotonic regression
International Conference on Acoustics, Speech, and Signal Processing, 2002The concept of locally monotonic regression is extended by considering metrics on r/sup n/ that are different from the Euclidean metric. The existence of regressions for a large class of metrics is shown. Algorithms that show the computability of locally monotonic regressions are given.
Alfredo Restrepo Palacios +2 more
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A Simple Method for Pairwise Monotone Regression
Psychometrika, 1975A simple method of monotone regression is described based on the principle of minimizing pairwise departures from monotonicity.
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Non-monotonic Feature Selection for Regression
2014Feature selection is an important research problem in machine learning and data mining. It is usually constrained by the budget of the feature subset size in practical applications. When the budget changes, the ranks of features in the selected feature subsets may also change due to nonlinear cost functions for acquisition of features. This property is
Haiqin Yang +3 more
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2010
In some applications, we require a monotone estimate of a regression function. In others, we want to test whether the regression function is monotone. For solving the first problem, Ramsay's, Kelly and Rice's, as well as point-wise monotone regression functions in a spline space are discussed and their properties developed. Three monotone estimates are
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In some applications, we require a monotone estimate of a regression function. In others, we want to test whether the regression function is monotone. For solving the first problem, Ramsay's, Kelly and Rice's, as well as point-wise monotone regression functions in a spline space are discussed and their properties developed. Three monotone estimates are
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A Solution to the Problem of Monotone Likelihood in Cox Regression
Biometrics, 2001Summary.The phenomenon of monotone likelihood is observed in the fitting process of a Cox model if the likelihood converges to a finite value while at least one parameter estimate diverges to ±∞. Monotone likelihood primarily occurs in small samples with substantial censoring of survival times and several highly predictive covariates.
Heinze, Georg, Schemper, Michael
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A Boosting Approach to Generalized Monotonic Regression
2007We propose a novel approach for generalized additive regression problems, where one or more smooth components are assumed to have monotonic influence on the dependent variable. The response is allowed to follow a simple exponential family. Smooth estimates are obtained by expansion of the unknown functions into B-spline basis functions, where the ...
Florian Leitenstorfer, Gerhard Tutz 0001
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Monotone Regression: Continuity and Differentiability Properties
Psychometrika, 1971Least-squares monotone regression has received considerable discussion and use. Consider the residual sum of squares Q obtained from the least-squares monotone regression of yi on xi. Treating Q as a function of the yi, we prove that the gradient ▽Q exists and is continuous everywhere, and is given by a simple formula.
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