Linear Transformations Preserving Best Linear Unbiased Estimators in a General Gauss-Markoff Model
نویسندگان
چکیده
منابع مشابه
Best Linear Unbiased Estimation in Linear Models
where X is a known n × p model matrix, the vector y is an observable ndimensional random vector, β is a p × 1 vector of unknown parameters, and ε is an unobservable vector of random errors with expectation E(ε) = 0, and covariance matrix cov(ε) = σV, where σ > 0 is an unknown constant. The nonnegative definite (possibly singular) matrix V is known. In our considerations σ has no role and hence ...
متن کاملThe equalities of ordinary least-squares estimators and best linear unbiased estimators for the restricted linear model
We investigate in this paper a variety of equalities for the ordinary least-squares estimators and the best linear unbiased estimators under the general linear (Gauss-Markov) model {y, Xβ, σΣ} and the restrained model {y, Xβ |Aβ = b, σΣ}.
متن کاملBest linear unbiased estimation and prediction under a selection model.
Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the prob...
متن کاملOn equality and proportionality of ordinary least squares, weighted least squares and best linear unbiased estimators in the general linear model
Equality and proportionality of the ordinary least-squares estimator (OLSE), the weighted least-squares estimator (WLSE), and the best linear unbiased estimator (BLUE) for Xb in the general linear (Gauss–Markov) model M 1⁄4 fy;Xb; sRg are investigated through the matrix rank method. r 2006 Elsevier B.V. All rights reserved. MSC: Primary 62J05; 62H12; 15A24
متن کاملLinear transformations preserving log-concavity
In this paper, we prove that the linear transformation yi = i ∑ j=0 ( m+ i n+ j ) xj , i = 0, 1, 2, . . . preserves the log-concavity property. © 2002 Elsevier Science Inc. All rights reserved.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1981
ISSN: 0090-5364
DOI: 10.1214/aos/1176345533