Mean and Variance Estimation by Kriging
Tomasz Suslo
Abstract
The aim of the paper is to derive the numerical least-squares estimator for mean and variance of random variable. In order to do so the following questions have to be answered: (i) what is the statistical model for the estimation procedure? (ii) what are the properties of the estimator, like optimality (in which class) or asymptotic properties? (iii) how does the estimator work in practice, how compared to competing estimators?
Create a lesson
Related papers
Sensitivity Calculus and its Numerical Implementation for Multi-D Hyperbolic Balance Laws
Olivia Dreßen, Michael Herty, Adrian Kolb et al.
A Unified Framework for Wasserstein Convergence of ULMC Methods beyond Log-Concavity: Old and New
Wanjie Lyu, Xiaojie Wang, Bin Yang
Beyond PINNs: A Unified Gauss--Newton and Petrov--Galerkin Framework for Neural and Hybrid PDE Solvers
Nilo Schwencke, Roland Maier
Incremental Column Subset Selection via Conditional Determinantal Point Processes
Laura Grigori, Zhipeng Xue
A Hybrid High-Order Method for the Elasticity Problem with Linear Slip Interface
Erik Burman, Peiqi Huang
Adaptive Sparse-grid Discontinuous Galerkin Approximations the Bhatnagar--Gross--Krook Model
Stefan Schnake, Miroslav Stoyanov, Eirik Endeve et al.