Mean and Variance Estimation by Kriging

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?Comment: 3 pages, 1 figure, source code (combo.pas) and input file (inp.dat) attache

Kriging Scenario For Capital Markets

An introduction to numerical statistics.Comment: 5 pages, 3 figures, attachments: source code and input file

MEAN AND VARIANCE ESTIMATION BY KRIGING

Abstract. The aim of the paper is to derive the numerical least-squares estimator for an unknown constant 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? 1

MEAN AND VARIANCE ESTIMATION BY KRIGING

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? 1

2 σ

2 Mean and variance estimation by kriging in the n + 1 unknowns: the kriging weights ωi j and the Lagrange parameter µj. Solving this system of equations, we can find a disjunction of the minimized error variance (the minimized mean squared error of estimation) σ 2 j = E{[Vj − ˆ Vj] 2} − E 2 {Vj − ˆ