Convex support vector regression

Article Management

Variable selection in convex quantile regression: L1-norm or L0-norm regularization?

Sheng Dai

Summary: This paper proposes a new L-0-norm regularization approach to address the curse of dimensionality and validates its effectiveness through practical application and Monte Carlo study.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2023)

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Article Computer Science, Software Engineering

An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems

Meixia Lin et al.

Summary: The paper presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function by solving a constrained convex quadratic programming problem. By designing proxALM method and constraint generation method, efficient solutions to large-scale convex QP problems are achieved, with significant reduction in computation time demonstrated in comprehensive numerical experiments.

MATHEMATICAL PROGRAMMING COMPUTATION (2022)

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Article Statistics & Probability

Inference for Local Parameters in Convexity Constrained Models

Hang Deng et al.

Summary: This article develops automated inference methods for local parameters in a collection of convexity constrained models based on a pivotal joint limit distribution theory. The proposed confidence intervals for various specific convexity constrained models are shown to have asymptotically exact coverage and Oracle length, requiring no additional information than the estimator itself. Simulation evidence and real data applications support the theoretical results and illustrate the usefulness of the proposed inference proposals.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2022)

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Article Computer Science, Interdisciplinary Applications

Multi-output Support Vector Frontiers

Daniel Valero-Carreras et al.

Summary: This paper demonstrates how FDH and DEA can be seen as special cases of Support Vector Regression, introduces a robust concept of technical efficiency, and shows through computational experience that the new approach outperforms FDH and DEA in terms of performance.

COMPUTERS & OPERATIONS RESEARCH (2022)

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Article Management

Shadow prices and marginal abatement costs: Convex quantile regression approach

Timo Kuosmanen et al.

Summary: This paper argues that most empirical studies over-estimate marginal abatement costs (MAC) and proposes a new MAC estimation method. Empirical analysis on electric power plants demonstrates that the upward bias of DEA may be a serious issue in real-world applications.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2021)

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Article Management

Support vector frontiers: A new approach for estimating production functions through support vector machines

Daniel Valero-Carreras et al.

Summary: This paper introduces an improved method Support Vector Frontiers (SVF) in estimating production functions, which overcomes the overfitting issue and reduces bias associated with the estimation of the true production function. The research also suggests standard FDH and DEA can be reinterpreted as Support Vector Regression techniques.

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE (2021)

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Article Economics

Industrial agglomeration effect for energy efficiency in Japanese production plants

Kenta Tanaka et al.

Summary: This study analyzes the relationship between industrial agglomeration effect and energy efficiency in Japan's paper/pulp and cement industries using plant-level data. The results show that energy efficiency in both industries improved between 2000 and 2010, although the factors for improvement and the impact of industrial agglomeration differ. The study highlights the importance of considering regional circumstances when seeking to improve energy efficiency.

ENERGY POLICY (2021)

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Article Computer Science, Interdisciplinary Applications

Sparse Convex Regression

Dimitris Bertsimas et al.

Summary: The study addresses the problem of best k-subset convex regression using n observations in d variables, presenting scalable algorithms for both sparse and non-sparse cases. The algorithms show promising results when compared to state-of-the-art methods, providing high-quality solutions in practical times. Additionally, the methods are effective in controlling the false discovery rate.

INFORMS JOURNAL ON COMPUTING (2021)

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Article Economics

Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries

Daisuke Yagi et al.

JOURNAL OF BUSINESS & ECONOMIC STATISTICS (2020)

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Article Statistics & Probability

On Degrees of Freedom of Projection Estimators With Applications to Multivariate Nonparametric Regression

Xi Chen et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2020)

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Article Management

Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method

Jose Luis Preciado Arreola et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2020)

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Article Economics

Conditional Yardstick Competition in Energy Regulation

Timo Kuosmanen et al.

ENERGY JOURNAL (2020)

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Article Development Studies

How much climate policy has cost for OECD countries?

Timo Kuosmanen et al.

WORLD DEVELOPMENT (2020)

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Article Statistics & Probability

A Computational Framework for Multivariate Convex Regression and Its Variants

Rahul Mazumder et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2019)

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Article Statistics & Probability

ESTIMATION BOUNDS AND SHARP ORACLE INEQUALITIES OF REGULARIZED PROCEDURES WITH LIPSCHITZ LOSS FUNCTIONS

Pierre Alquier et al.

ANNALS OF STATISTICS (2019)

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Article Statistics & Probability

Nonparametric Shape-Restricted Regression

Adityanand Guntuboyina et al.

STATISTICAL SCIENCE (2018)

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Article Statistics & Probability

Shape Constraints in Economics and Operations Research

Andrew L. Johnson et al.

STATISTICAL SCIENCE (2018)

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Article Statistics & Probability

On Univariate Convex Regression

Promit Ghosal et al.

SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY (2017)

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Article Statistics & Probability

FAITHFUL VARIABLE SCREENING FOR HIGH-DIMENSIONAL CONVEX REGRESSION

Min Xu et al.

ANNALS OF STATISTICS (2016)

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Article Management

Nonparametric quantile frontier estimation under shape restriction

Yongqiao Wang et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2014)

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Article Computer Science, Interdisciplinary Applications

On Convergence Rates of Convex Regression in Multiple Dimensions

Eunji Lim

INFORMS JOURNAL ON COMPUTING (2014)

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Article Management

A more efficient algorithm for Convex Nonparametric Least Squares

Chia-Yen Lee et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2013)

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Article Computer Science, Artificial Intelligence

Estimating α-frontier technical efficiency with shape-restricted kernel quantile regression

Yongqiao Wang et al.

NEUROCOMPUTING (2013)

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Article Management

One-stage and two-stage DEA estimation of the effects of contextual variables

Andrew L. Johnson et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2012)

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Article Computer Science, Artificial Intelligence

Multivariate convex support vector regression with semidefinite programming

Yongqiao Wang et al.

KNOWLEDGE-BASED SYSTEMS (2012)

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Article Economics

Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model

Timo Kuosmanen

ENERGY ECONOMICS (2012)

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Article Statistics & Probability

NONPARAMETRIC LEAST SQUARES ESTIMATION OF A MULTIVARIATE CONVEX REGRESSION FUNCTION

Emilio Seijo et al.

ANNALS OF STATISTICS (2011)

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Article Computer Science, Information Systems

Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block l1/l∞-Regularization

Sahand N. Negahban et al.

IEEE TRANSACTIONS ON INFORMATION THEORY (2011)

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Article Statistics & Probability

VIF Regression: A Fast Regression Algorithm for Large Data

Dongyu Lin et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2011)

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Article Management

Data Envelopment Analysis as Nonparametric Least-Squares Regression

Timo Kuosmanen et al.

OPERATIONS RESEARCH (2010)

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Article Statistics & Probability

THE COMPOSITE ABSOLUTE PENALTIES FAMILY FOR GROUPED AND HIERARCHICAL VARIABLE SELECTION

Peng Zhao et al.

ANNALS OF STATISTICS (2009)

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Article Economics

Representation theorem for convex nonparametric least squares

Timo Kuosmanen

ECONOMETRICS JOURNAL (2008)

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Article Statistics & Probability

Regularization and variable selection via the elastic net

H Zou et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2005)

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Review Computer Science, Theory & Methods

A tutorial on support vector regression

AJ Smola et al.

STATISTICS AND COMPUTING (2004)

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Convex support vector regression

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