The Numerical Generalized Least-Squares Estimator of an Unknown Constant Mean of Random Field

نویسنده

  • Tomasz Suslo
چکیده

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. 1. The best linear unbiased generalized statistics Remark. To simplify notation we use Einstein summation convention then n ∑ i=1 ω jρij = ω i jρij = w ′r where

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The Correct Classic Generalized Least-Squares Estimator of an Unknown Constant Mean of Randon Field

The aim of the paper is to derive for the negative correlation function with a time parameter an asymptotic disjunction limj→∞ ωi jvi of the numerical generalized least-squares estimator ωi jvi 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.

متن کامل

Efficiency Comparisons in Multivariate Multiple Regression with Missing Outcomes

We consider a follow-up study in which an outcome variable is to be measured at fixed time points and covariate values are measured prior to start of follow-up. We assume that the conditional mean of the outcome given the covariates is a linear function of the covariates and is indexed by occasion-specific regression parameters. In this paper we study the asymptotic properties of several freque...

متن کامل

Bootstrap of a Semiparametric Partially Linear Model with Autoregressive Errors

This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable serially correlated random errors. The random errors are modeled by an autoregressive time series. We show that the distributions of the feasible semiparametric generalized least squares estimator o...

متن کامل

Determination of the best-fitting reference orbit for a LEO satellite using the Lagrange coefficients

Linearization of the nonlinear equations and iterative solution is the most well-known scheme in many engineering problems. For geodetic applications of the LEO satellites, e.g. the Earth’s gravity field recovery, one needs to provide an initial guess of the satellite location or the so-called reference orbit. Numerical integration can be utilized for generating the reference orbit if a satelli...

متن کامل

A New Stochastic Restricted Biased Estimator under Heteroscedastic or Correlated Error

In this paper, under the linear regression model with heteroscedastic and/or correlated errors when the stochastic linear restrictions on the parameter vector are assumed to be held, a generalization of the ordinary mixed estimator (GOME), ordinary ridge regression estimator (GORR) and Generalized least squares estimator (GLSE) is proposed. The performance of this new estimator against GOME, GO...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1111.3971  شماره 

صفحات  -

تاریخ انتشار 2011