On constructing almost unbiased estimators of finite population mean using transformed auxiliary variable

2001 ◽  
Vol 42 (4) ◽  
pp. 505-515
Author(s):  
Horng-Jinh Chang ◽  
Kuo-Chung Huang
Keyword(s):  
Finite Population ◽  
Auxiliary Variable ◽  
Population Mean ◽  
Unbiased Estimators ◽  
Almost Unbiased

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.

Almost unbiased ratio-cum-product estimators for the finite population mean

Test ◽  
1992 ◽  
Vol 1 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Housila P. Singh ◽  
R. S. Biradar
Keyword(s):  
Finite Population ◽  
Population Mean ◽  
Almost Unbiased

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 Efficiency of Almost Unbiased Mean Imputation When Population Mean of Auxiliary Variable is UnknownOn The Efficiency of Almost Unbiased Mean Imputation When Population Mean of Auxiliary Variable is Unknown

2021 ◽  
pp. 235-250
Author(s):  
A. Audu ◽  
A. Danbaba ◽  
S. K. Ahmad ◽  
N. Musa ◽  
A. Shehu ◽  
...  
Keyword(s):  
Order Approximation ◽  
Taylor Series Expansion ◽  
Auxiliary Variable ◽  
Missing Observations ◽  
Population Mean ◽  
Mean Imputation ◽  
Expansion Technique ◽  
Class Of Estimators ◽  
First Order Approximation ◽  
Almost Unbiased

Human-assisted surveys, such as medical and social science surveys, are frequently plagued by non-response or missing observations. Several authors have devised different imputation algorithms to account for missing observations during analyses. Nonetheless, several of these imputation schemes' estimators are based on known population meanof auxiliary variable. In this paper, a new class of almost unbiased imputation method that uses  as an estimate of is suggested. Using the Taylor series expansion technique, the MSE of the class of estimators presented was derived up to first order approximation. Conditions were also specified for which the new estimators were more efficient than the other estimators studied in the study. The results of numerical examples through simulations revealed that the suggested class of estimators is more efficient.

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