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Full Length Article
American Journal of Business and Operations Research
Volume 6 , Issue 2, PP: 08-15 , 2022 | Cite this article as | XML | Html |PDF

Title

An Innovative Additive Mathematical Model Using Auxiliary Information

Authors Names :   Tanveer A. Tarray   1 *     Javid Gani Dar   2     Ishfaq S. Ahmad   3  

1  Affiliation :  Department of Mathematical Science, Islamic University of Science and Technology, Jammu and Kashmir, India

    Email :  tanveerstat@gmail.com


2  Affiliation :  Department of Mathematical Science, Islamic University of Science and Technology, Jammu and Kashmir, India

    Email :   javinfo.stat@yahoo.co.in


3  Affiliation :  Department of Mathematical Science, Islamic University of Science and Technology, Jammu and Kashmir, India

    Email :  peerishfaq007@gmail.com



Doi   :   https://doi.org/10.54216/AJBOR.060201

Received: January 12, 2022 Accepted: March 25, 2022

Abstract :

This article proposes innovative ratio and regression estimators based on additive randomized response model. Expressions for the biases and mean squared errors of the recommended estimators are derived. It has been revealed that the advised groundbreaking ratio and regression estimators are improved than ratio and regression estimators under a very realistic condition. Numerical illustrations and simulation study are also given in support of the present study.

Keywords :

Estimation; Mean Square error; Bias; Auxiliary variable; RRM.

AMS Subject Classification: 62D05.

References :

[1]                 Eichhorn B.H. and Hayre .LS. (1983): Scrambled randomized response methods for         obtaining sensitive quantitative dada. Jour. Statist. Plann. Inf. 7,307-316.

[2] Himmelfarb S. and Edgell S.E. (1980): Additive constant model: a randomized  response technique for eliminating evasiveness to quantitative response           questions. Psychol.              Bull. 87, 525-530.

[3] Pandey  B.N. (1979): On shrinkage estimation of normal population variance.  Commun. Statist. Theo. and               Metho., 8, 359-365.

[4] Panday and Singh (1977): Estimation of variance of normal population using prior           information. Jour. Ind. Statist. Assoc.,15,141-150.

[5] Pollock K.H. and Bek Y. (1976): A comparison of three randomized response models  for quantitative data. Jour. Amer. Statist. Assoc., 71, 884-886.

[6] Singh H. P. and Tarray T. A. (2012): A Stratified Unknown repeated trials   in             randomized response sampling. Common. Korean Statist. Soc., 19,  (6),             751-759.

 

[7] Singh H.P. and Tarray T.A. (2013): An alternative to Kim and Warde’s mixed  randomized response model. Statist. Oper. Res.  Trans.(SORT),                                  37 (2),                     189-210.

[8] Singh H.P. and Tarray T.A. (2014): An Improvement Over Kim and Elam   Stratified        Unrelated Question Randomized Response Model Using Neyman Allocation.        Sankhya – B, The Ind. Jour. Statist.,DOI 10.1007/s13571-014-0088-5.

[9] Singh H.P., Jong M. Kim and Tarray T.A. (2015): A family of estimators of  population variance in two occasion rotation patterns. Comm. Statist. Theo.   Meth.., 45(14) 4106 - 4116 .

[10]               Singh HP and Tarray TA (2017): A stratified unrelated question randomized  response model using Neyman allocation. Comm. Statist. Theo.- Metho. DOI:                  10.1080/03610926.2014.983612.

[11]               Tarray T.A. and Singh H.P. (2015): A general procedure for estimating the mean of a sensitive variable using auxiliary information. Investigacion Operacionel.  36(3),249-262.

[12]               Tarray T.A. and Singh H.P. (2015): A randomized response model for  estimating   a         rare         sensitive attribute in stratified sampling using Poisson distribution.                 Model    Assist. Statist. Appl., 10, 345-360.

[13]               Tarray TA and Singh HP  (2016): New procedures of estimating proportion and  sensitivity using randomized response in a dichotomous finite population. Jour.  Mod. Appli. Statist. Meth. 15(1), 635-669.

[14]               Tarray TA (2017): Scrutinize on Stratified Randomized Response Technique, Munich,     GRIN Verlag, ISBN: 9783668554580.

[15]               Tarray T.A. and Singh H.P. (2018): Missing data in clinical trials: stratified  Singh and  Grewal’s randomized response model using geometric  distribution. Trends in     Bioinformatics, 11(1),44-55.

[16]               Tarray T.A., Sigh H.P. and Saadia Masood (2019): An Endowed  Randomized  Response                Model for Estimating a Rare Sensitive  Attribute  Using Poisson        Distribution.          Trends in  Applied  Sciences  Research, 12, 1-6.


Cite this Article as :
Tanveer A. Tarray , Javid Gani Dar , Ishfaq S. Ahmad, An Innovative Additive Mathematical Model Using Auxiliary Information, American Journal of Business and Operations Research, Vol. 6 , No. 2 , (2022) : 08-15 (Doi   :  https://doi.org/10.54216/AJBOR.060201)