Modern Statistics by Kriging

We present statistics (S-statistics) based only on random variable (not random value) with a mean squared error of mean estimation as a concept of error.Comment: 4 page

Complex Mean and Variance of Linear Regression Model for High-Noised Systems by Kriging

The aim of the paper is to derive the complex-valued least-squares estimator for bias-noise mean and variance.Comment: 3 page

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

MODERN STATISTICS BY KRIGING

Abstract. We present statistics (S-statistics) based only on random variable (not random value) with a mean squared error of mean estimation as a concept of error. Remark. Notation is equivalent to where Notation is equivalent to where ω = ω i j = n∑ i=1 l=1 n∑ i=1 ω1 j ωn j 1. Origin ω i jρij ω i j ρij = ω ′ ρ, n×1 n∑ ω i jω l jρil =, ρ = ρij = ω i j ρiiω i j n∑ ρil = Λ = Λ ′ = ρii = n∑ ρ1j ρnj n×

MODERN STATISTICS BY KRIGING

The primitive statistics can only cut flowers in garden (to remove spatial dependence) measure and compute central value. Weighted average is strictly restricted to central value. The idea of modern statistics is much more intelligent not to cut but to consider spatially dependent mathematical model of garden with predictable spread values. Weighted average is not strictly restricted to central value. A name of technique known as kriging – the Theory of Regionalised Variables (G. Matheron, in the early 1960’s) – is often associated with the acronym BLUE for ”best linear unbiased estimator”. Kriging is ”linear ” because its estimates are weighted averages of the known values; it is ”unbiased” since it tries to have the mean of the estimation error for a random process model equal to 0 (I condition); it is ”best ” because it aims at minimizing the modeled error variance (II condition). Kriged estimate is strictly restricted to central value (mean) only if som