Minimal Complete Class of Linear Unbiased Estimators of Population Mean for a Special Sampling Design1

1989 ◽  
Vol 38 (1-2) ◽  
pp. 71-82
Author(s):  
J. A. Patel ◽  
H. C. Patel
Keyword(s):  
Finite Population ◽  
Positive Probability ◽  
Complete Class ◽  
Population Mean ◽  
Unbiased Estimators ◽  
Homogeneous Linear ◽  
Special Case

In this paper we give a complete description of the minimal complete subclass of C the class of all homogeneous linear unbiased estimators of a finite population mean for the extremely special case of taking sample of size 2 units from a population of size 4, where only samples containing units ( U1, Ui+ 1) have equal positive probability.

On asymptotically design-unbiased estimators of a finite population mean

Biometrika ◽  
1991 ◽  
Vol 78 (1) ◽  
pp. 189-195 ◽  
Author(s):  
LIH-YUAN DENG ◽  
RAJ S. CHHIKARA
Keyword(s):  
Finite Population ◽  
Population Mean ◽  
Unbiased Estimators

scholarly journals On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model

2011 ◽  
Vol 1 (3) ◽  
pp. 280-285 ◽  
Author(s):  
Lars Sjöberg
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Minimum Bias ◽  
Unbiased Estimators ◽  
Independent Observations ◽  
Special Case

On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component ModelVariance components (VCs) in linear adjustment models are usually successfully computed by unbiased estimators. However, for many unbiased VC techniques estimated variance components might be negative, a result that cannot be tolerated by the user. This is, for example, the case with the simple additive VC model aσ2/1 + bσ2/2 with known coefficients a and b, where either of the unbiasedly estimated variance components σ2/1 + σ2/2 may frequently come out negative. This fact calls for so-called non-negative VC estimators. Here the Best Quadratic Minimum Bias Non-negative Estimator (BQMBNE) of a two-variance component model is derived. A special case with independent observations is explicitly presented.

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