On a class of interval predictor models with universal reliability

DOI10.1016/j.automatica.2019.108542zbMath1429.93064OpenAlexW2974431408MaRDI QIDQ2280963

Marco C. Campi, Algo Carè, Simone Garatti

Publication date: 19 December 2019

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11311/1121161

zbMATH Keywords

convex optimizationstatistical learningset-valued modelsminimax regressioninterval prediction

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Convex programming (90C25) System identification (93B30)

Related Items (4)

Probabilistic feasibility guarantees for convex scenario programs with an arbitrary number of discarded constraints ⋮ On Conditional Risk Assessments in Scenario Optimization ⋮ Unnamed Item ⋮ A theory of the risk for empirical CVaR with application to portfolio selection

Uses Software

• AS 135

Cites Work

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• A sampling-and-discarding approach to chance-constrained optimization: feasibility and Optimality
• Identification of parametric models from experimental data. Transl. from an upd. French version by the authors, with the help of John Norton
• Learning noisy functions via interval models
• Interval predictor models: identification and reliability
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• Linear Curve Fitting Using Least Deviations
• Linear Programming Techniques for Regression Analysis
• The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
• Scenario Min-Max Optimization and the Risk of Empirical Costs
• Algorithm AS 135: Min-Max Estimates for a Linear Multiple Regression Problem
• Algorithm 495: Solution of an Overdetermined System of Linear Equations in the Chebychev Norm [F4]
• The Method of Least Squares and Some Alternatives: Part III
• Guaranteed recursive non-linear state bounding using interval analysis
• Introduction to the Scenario Approach
• Strict Discrete Approximation in the L 1 and L ∞ Norms
• RANDOM PREDICTOR MODELS FOR RIGOROUS UNCERTAINTY QUANTIFICATION
• The Scenario Approach to Robust Control Design
• Convex Analysis

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