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A dual active set algorithm for optimal sparse convex regression
Vestnik Samarskogo Gosudarstvennogo Tehničeskogo Universiteta. Seriâ: Fiziko-Matematičeskie Nauki, 2019 The shape-constrained problems in statistics have attracted much attention in recent decades. One of them is the task of finding the best fitting monotone regression. The problem of constructing monotone regression (also called isotonic regression) is to
Aleksandr A. Gudkov +3 more
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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
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Water Science and Technology, 2023 Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble ...
Jitendra Rajput +6 more
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LASSO Isotone for High-Dimensional Additive Isotonic Regression [PDF]
Journal of Computational and Graphical Statistics, 2012 Additive isotonic regression attempts to determine the relationship between a multi-dimensional observation variable and a response, under the constraint that the estimate is the additive sum of univariate component effects that are monotonically increasing. In this article, we present a new method for such regression called LASSO Isotone (LISO).
Fang, Z, Meinshausen, N
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A geometrical approach to Iterative Isotone Regression [PDF]
Applied Mathematics and Computation, 2014 25 pages, 5 ...
Arnaud Guyader +3 more
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CVXR: An R Package for Disciplined Convex Optimization
Journal of Statistical Software, 2020 CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather than the ...
Anqi Fu +2 more
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Optimal Reduced Isotonic Regression
CoRR, 2014 Isotonic regression is a shape-constrained nonparametric regression in which the regression is an increasing step function. For $n$ data points, the number of steps in the isotonic regression may be as large as $n$. As a result, standard isotonic regression has been criticized as overfitting the data or making the representation too complicated.
Janis Hardwick, Quentin F. Stout
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Bayesian Isotonic Regression and Trend Analysis [PDF]
Biometrics, 2004 Summary. In many applications, the mean of a response variable can be assumed to be a nondecreasing function of a continuous predictor, controlling for covariates. In such cases, interest often focuses on estimating the regression function, while also assessing evidence of an association.
Neelon, Brian, Dunson, David B.
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Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints
Applied SciencesIntegrating domain knowledge is increasingly recognized as vital for improving the relevance and reliability of machine learning models. This integration is often implemented through specific types of constraints that reflect real-world conditions or ...
Doowon Choi
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Isotonic regression analysis of Guzerá cattle growth curves
Revista CeresThe objective of this study was to apply data transformation via isotonic regression in growth curves studies of Guzerá cattle whose data presented disturbances characterized by decreased body weight in certain age groups.
Adriano Rodrigues +5 more
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