Distributionally robust portfolio optimization with second-order stochastic dominance based on wasserstein metric
In portfolio optimization, we may be dealing with misspecification of a known distribution,
that stock returns follow it. The unknown true distribution is considered in terms of a …
that stock returns follow it. The unknown true distribution is considered in terms of a …
Portfolio optimization using robust mean absolute deviation model: Wasserstein metric approach
Z Hosseini-Nodeh, R Khanjani-Shiraz… - Finance Research …, 2023 - Elsevier
Portfolio optimization can lead to misspecified stock returns that follow a known distribution.
To investigate tractable formulations of the portfolio selection problem, we study these …
To investigate tractable formulations of the portfolio selection problem, we study these …
Distributionally robust chance-constrained kernel-based support vector machine
Support vector machine (SVM) is a powerful model for supervised learning. This article
addresses the nonlinear binary classification problem using kernel-based SVM with …
addresses the nonlinear binary classification problem using kernel-based SVM with …
Distributionally robust joint chance-constrained programming: Wasserstein metric and second-order moment constraints
In this paper, we propose a new approximate linear reformulation for distributionally robust
joint chance programming with Wasserstein ambiguity sets and an efficient solution …
joint chance programming with Wasserstein ambiguity sets and an efficient solution …
A novel robust optimization model for nonlinear Support Vector Machine
F Maggioni, A Spinelli - European Journal of Operational Research, 2024 - Elsevier
In this paper, we present new optimization models for Support Vector Machine (SVM), with
the aim of separating data points in two or more classes. The classification task is handled …
the aim of separating data points in two or more classes. The classification task is handled …
A distributionally robust chance-constrained kernel-free quadratic surface support vector machine
This paper studies the problem of constructing a robust nonlinear classifier when the data
set involves uncertainty and only the first-and second-order moments are known a priori. A …
set involves uncertainty and only the first-and second-order moments are known a priori. A …
Robust chance-constrained geometric programming with application to demand risk mitigation
B Fontem - Journal of Optimization Theory and Applications, 2023 - Springer
We determine bounds on the optimal value for a chance-constrained program aiming to
minimize the worst-case probability that a certain nonlinear function with random exponents …
minimize the worst-case probability that a certain nonlinear function with random exponents …
A machine learning method based on stacking heterogeneous ensemble learning for prediction of indoor humidity of greenhouse
Efficient production management, high productivity, and improved product quality are
essential for the success of greenhouse production in producing sustainable agricultural …
essential for the success of greenhouse production in producing sustainable agricultural …
[图书][B] Distributionally Robust Optimization with Applications to Support Vector Machines
F Lin - 2023 - search.proquest.com
This dissertation research studies the distributionally robust optimization with applications to
support vector machines (SVMs) for robustly solving classification problems when the data is …
support vector machines (SVMs) for robustly solving classification problems when the data is …
Tight Bound for Sum of Heterogeneous Random Variables: Application to Chance Constrained Programming
We study a tight Bennett-type concentration inequality for sums of heterogeneous and
independent variables, defined as a one-dimensional minimization. We show that this …
independent variables, defined as a one-dimensional minimization. We show that this …