Results 81 to 90 of about 27,189 (192)
Orthogonality conditions for convex regression
Econometric identification generally relies on orthogonality conditions, which usually state that the random error term is uncorrelated with the explanatory variables. In convex regression, the orthogonality conditions for identification are unknown.
Dai, Sheng +2 more
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Modelling with twice continuously differentiable functions
Croatian Operational Research Review, 2014 Many real life situations can be described using twice continuously differentiable functions over convex sets with interior points. Such functions have an interesting separation property: At every interior point of the set they separate particular ...
Sanjo Zlobec
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Trends in Computational and Applied MathematicsRidge and Lasso regressions are types of linear regression, a machine learning tool for dealing with data. Based on multiobjective optimization theory, we transform Ridge and Lasso regression into bi-objective optimization problems. The Pareto fronts of
W. P. Freire
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Overfitting Reduction in Convex Regression
Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and many other areas. However, it has been empirically observed that convex regression produces inconsistent estimates of convex functions and extremely large subgradients near ...
Liao, Zhiqiang +3 more
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Achieving the oracle property of OEM with nonconvex penalties
Statistical Theory and Related Fields, 2017 Thepenalised least square estimator of non-convex penalties such as the smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) is highly nonlinear and has many local optima.
Shifeng Xiong, Bin Dai, Peter Z. G. Qian
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First-order methods of smooth convex optimization with inexact oracle [PDF]
In this paper, we analyze different first-order methods of smooth convex optimization employing inexact first-order information. We introduce the notion of an approximate first-order oracle.
GLINEUR, François +2 more
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Softplus Regressions and Convex Polytopes
, 2016 33 pages + 12 page appendix, 15 figures, 10 ...
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On Convex Combination of Local Constant Regression
Communications for Statistical Applications and Methods, 2006 Local polynomial regression is widely used because of good properties such as such as the adaptation to various types of designs, the absence of boundary effects and minimax efficiency Choi and Hall (1998) proposed an estimator of regression function using a convex combination idea. They showed that a convex combination of three local linear estimators
Jung-Won Mun, Choong-Rak Kim
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Multi-task learning regression via convex clustering
Computational Statistics & Data AnalysisMulti-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL, there are several assumptions for the relationships and methods to incorporate them.
Akira Okazaki, Shuichi Kawano
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Optimal resource allocation: Convex quantile regression approach
European Journal of Operational ResearchOptimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop quantile allocation models to examine how much the output and productivity could potentially increase if ...
Sheng Dai +3 more
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