Journal ArticleDOI
Shape Constraints in Economics and Operations Research
TLDR
This paper briefly reviews an illustrative set of research utilizing shape constraints in the economics and operations research literature and highlights the methodological innovations and applications with a particular emphasis on utility functions, production economics and sequential decision making applications.Abstract:
Shape constraints, motivated by either application-specific assumptions or existing theory, can be imposed during model estimation to restrict the feasible region of the parameters. Although such restrictions may not provide any benefits in an asymptotic analysis, they often improve finite sample performance of statistical estimators and the computational efficiency of finding near-optimal control policies. This paper briefly reviews an illustrative set of research utilizing shape constraints in the economics and operations research literature. We highlight the methodological innovations and applications, with a particular emphasis on utility functions, production economics and sequential decision making applications.read more
Citations
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Journal ArticleDOI
Order Restricted Statistical Inference.
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The collective model of household consumption: A nonparametric characterization
TL;DR: In this paper, the authors provide a nonparametric characterization of a general collective model for household consumption, which includes externalities and public consumption, and establish testable necessary and sufficient conditions for data consistency with collective rationality that only include observed price and quantity information.
Proceedings Article
Hard Shape-Constrained Kernel Machines
TL;DR: In this paper, the authors prove that hard affine shape constraints on function derivatives can be encoded in kernel machines, which represent one of the most flexible and powerful tools in machine learning and statistics.
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Multivariate Convex Regression at Scale
Wenyu Chen,Rahul Mazumder +1 more
TL;DR: This framework can solve instances of the convex regression problem with $n=10^5$ and $d=10$---a QP with 10 billion variables---within minutes; and offers significant computational gains compared to current algorithms.
References
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Journal ArticleDOI
An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution
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Journal ArticleDOI
Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion
Andy Philpott,V. L. de Matos +1 more
TL;DR: The incorporation of a time-consistent coherent risk measure into a multi-stage stochastic programming model is considered, so that the model can be solved using a SDDP-type algorithm.
Journal ArticleDOI
Semiparametric Regression for the Applied Econometrician
TL;DR: In this paper, the authors discuss only a very small subset of the possible statistical methods and designs available and discuss the importance of randomizing the run order of an experiment to obtain valid and interpretable results, the distinction between replicates and repeated observations, or the role of blocking or restrictions on randomization to run an experiment more effectively.
Journal ArticleDOI
Representation Theorem for Convex Nonparametric Least Squares
TL;DR: In this paper, a nonparametric least-squares regression model that endogenously selects the functional form of the regression function from the family of continuous, monotonic increasing and globally concave functions that can be nondifferentiable is examined.
Journal ArticleDOI
Shape constrained additive models
Natalya Pya,Simon N. Wood +1 more
TL;DR: A framework is presented for generalized additive modelling under shape constraints on the component functions of the linear predictor of the GAM by mildly non-linear extensions of P-splines that facilitates efficient estimation of smoothing parameters as an integral part of model estimation.