Results 111 to 120 of about 2,879,792 (277)
Global optimization of Lipschitz functions
, 2017 The goal of the paper is to design sequential strategies which lead to efficient optimization of an unknown function under the only assumption that it has a finite Lipschitz constant. We first identify sufficient conditions for the consistency of generic sequential algorithms and formulate the expected minimax rate for their performance.
Cédric Malherbe, Nicolas Vayatis
openaire +3 more sources
A Splitting Architecture for Exact Reduced Coulomb Friction
Computer Graphics Forum, EarlyView.Abstract Existing approaches to frictional contact dynamics typically either modify the Coulomb law to improve numerical robustness or solve the exact law in a fully coupled monolithic form. However, in its reduced form, exact Coulomb friction can be written as a cone complementarity problem with an augmented velocity, which reveals a natural split ...
Hongcheng Song +3 more
wiley +1 more source
A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces
International Statistical Review, EarlyView.Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri +3 more
wiley +1 more source
(δ,γ)-Jacobi-Dunkl Lipschitz Functions in the Space L2(R,Aα,β(x)dx)
International Journal of Analysis and Applications, 2015 Using a generalized Jacobi-Dunkl translation, we obtain an analog of Theorem 5.2 in Younis paper [7] for the Jacobi-Dunkl transform for functions satisfying the (δ,γ)-Jacobi-Dunkl Lipschitz condition in the space L2(R,Aα,β(x)dx), α ≥ β ≥−1/2, α ≠−1/2.
R. Daher, S. El Ouadih
doaj +2 more sources
A note on generalised information criteria for structured sparse models
International Statistical Review, EarlyView.Summary We propose a generalised information criteria ( gic) that accounts for sparsity pattern in the model. We obtain both asymptotic and nonasymptotic results for model selection. Moreover, we show that the gic is useful for selecting the regularisation parameter in regularised m$$ m $$ estimation in high‐dimensional scenarios.
Eduardo Fonseca Mendes +1 more
wiley +1 more source
The extension of starshaped bounded Lipschitz functions
Journal of Numerical Analysis and Approximation Theory, 1980 Not available.
C. Mustăţa
doaj +2 more sources
Lipschitz measures and vector-valued Hardy spaces
International Journal of Mathematics and Mathematical Sciences, 2001 We define certain spaces of Banach-valued measures called Lipschitz measures. When the Banach space is a dual space X*, these spaces can be identified with the duals of the atomic vector-valued Hardy spaces HXp(ℝn ...
Magali Folch-Gabayet +2 more
doaj +1 more source
Fairness‐aware insurance pricing: A multi‐objective optimization approach
Journal of Risk and Insurance, EarlyView.Abstract Machine‐learning models can provide accurate predictions in insurance pricing, but can also increase disparities between protected groups. Existing fairness‐aware pricing approaches typically target one fairness notion at a time, making it difficult to compare trade‐offs between predictive accuracy, group fairness, individual fairness, and ...
Tim J. Boonen, Xinyue Fan, Zixiao Quan
wiley +1 more source
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka +4 more
wiley +1 more source
Density‐Valued ARMA Models by Spline Mixtures
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source