arXiv subjects
Publications and source records attributed to Tomasz Suslo.
The aim of the paper is to derive for the negative correlation function with a time parameter an asymptotic disjunction of the numerical generalized least-squares estimator of an unknown constant mean of random field in fact the correct classic generalized least-squares estimator of an unknown constant mean of the field.
In this paper the complex-valued best linear unbiased estimator of an unknown constant mean of white noise was derived the ordinary least-squares estimator of an unknown constant mean of random field (arithmetic mean) charged by an imaginary error.
We constraint on computer the best linear unbiased generalized statistics of random field for the best linear unbiased generalized statistics of an unknown constant mean of random field and derive the numerical generalized least-squares estimator of an unknown constant mean of random field. We derive the third constraint of spatial statistics and show that the classic generalized least-squares estimator of an unknown constant mean of the field is only an asymptotic disjunction of the numerical one.
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?
The aim of the paper is to derive the complex-valued least-squares estimator for bias-noise mean and variance.
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.