American Journal of Operational Research 2016, 6(2): 48-54 DOI: 10.5923/j.ajor.20160602.03 Improved Ratio-Cum-Product Estimators of Population Mean Using Known Population Parameters of Auxiliary Variables Subhash Kumar Yadav1, Jambulingam Subramani2, S. S. Mishra1,*, Alok Kumar Shukla3 1Department of Mathematics and Statistics (A Centre of Excellence), Dr. RML Avadh University, Faizabad, U.P., India 2Department of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, R V Nagar, Kalapet, Pondicherry, India 3Department of Statistics, D.A.V College, Kanpur, U.P., India Abstract In the present paper, a ratio-cum-product type estimator of finite population mean using known coefficient of kurtosis and median of auxiliary variable has been proposed. The explicit expressions for bias and mean squared error of the proposed estimator with large sample approximation are derived up to the first order of approximation. A comparison has been made with the existing estimators of population mean using auxiliary variable under simple random sampling scheme. An empirical study is also carried out to demonstrate the performance of the suggested estimator along with the existing estimators of population mean under simple random sampling. It has been shown through the empirical study that the proposed estimator has minimum mean squared error among all existing estimators of population mean. It is the best estimator of population mean among all existing estimators. Keywords Ratio-cum-product estimator, Median, Coefficient of kurtosis, Bias, Mean squared error N 1 N 1 means = and Xx= of x and x 1. Introduction Xx11∑ i 22∑ i 1 2 N i=1 N i=1 Use of auxiliary information has been in practice to respectively are known. increase the efficiency of the estimators. When population Usual ratio and product estimators given by Cochran [1] parameters of the auxiliary variable are known, several and Robson [12] respectively for estimating the population estimators for population mean of study variable have been mean Y respectively are defined as, discussed in the literature. When the variable under study and the auxiliary variables are highly and positively X1 yyR = (1.1) correlated and the line of regression passes through origin, x1 ratio method of estimation is preferred to use. On the other hand, if they are highly and negatively correlated, product x yy= 2 (1.2) method of estimation is suggested to use. P X 2 Let the finite population U= ( UU , ,..., U ) consists 12 N Upadhyaya and Singh [18] suggested ratio and product of N units. Suppose two auxiliary variables x1 and x2 estimators utilizing coefficient of variation and coefficient of kurtosis of auxiliary variables as are observed on Ui (iN= 1,2,..., ) , where x1 is positively and x is negatively correlated with the study XC1x + β 21() x 2 ty= 1 (1.3) variable y . A simple random sample of size n with n < N, is 1 xC+ β () x 1x1 21 drawn using simple random sampling without replacement (SRSWOR) from the population U to estimate the population xC2x + β 22() x ty= 2 (1.4) mean (Y ) of study character y , when the population 2 XC+ β () x 2x2 22 * Corresponding author: Xβ () xC+ 12 1 x1 [email protected] (S. S. Mishra) ty3 = (1.5) Published online at http://journal.sapub.org/ajor xβ () xC+ 12 1 x1 Copyright © 2016 Scientific & Academic Publishing. All Rights Reserved American Journal of Operational Research 2016, 6(2): 48-54 49 Tailor et.al [17] suggested two estimators of population x22β () xC 2+ x ty= 2 (1.6) mean using coefficients of variation and coefficients of 4 Xβ () xC+ 22 2 x2 kurtosis of auxiliary variables as, To estimate Y , Singh [16] suggested a XC1xx++ββ 21() x xC 2 22 ( x ) ty= 12 (1.9) ratio-cum-product estimator as 7 xC++ββ() x X C() x 1xx12 21 2 22 Xx ty= 12 (1.7) 5 X12ββ() xC 1++xx x22 ( x 2 ) C xX ty= 12 (1.10) 12 8 x12ββ() xC 1++xx X22() x 2 C Singh and Tailor [15] suggested a ratio-cum-product 12 estimator of Y utilizing the correlation coefficient between To the first degree of approximation the mean squared auxiliary variables as error (MSE) of the estimators yR , yP , t1 , t2 , t3 , t4 , , , and respectively are Xx12++ρρxx xx t5 t6 t7 t8 ty= 12 12 (1.8) 6 xX++ρρ 12xx12 xx12 22 2 MSE() y=θρ Y [ C +− C 2 C C ] (1.11) R y x1 yx 11 y x 22 2 MSE() y=θρ Y [ C ++ C 2 C C ] (1.12) P y x2 yx 22 y x 2 2 22 MSE() t=θ Y [ C +− λ C 2 ρλ C C ] (1.13) 1y 11 x11 yx y x 1 2 2 22 MSE() t=θ Y [ C ++ λ C 2 ρλ C C ] (1.14) 2y 22 x22 yx y x 2 2 2 22 MSE() t=θ Y [ C +− γ C 2 ρλ C C ] (1.15) 3y 11 x11 yx y x 1 2 2 22 MSE() t=θ Y [ C ++ γ C 2 ργ C C ] (1.16) 4y 22 x22 yx y x 2 22 2 2 MSE( t )=θ Y [ C +− C (1 2 κ ) +C {1 + 2 ( κκ − ) } ] (1.17) 5 y x1 yx 1 x 2 yx2 x 12 x 22 2 2 MSE( t )=θ Y [ C + µ C ( µ −+ 2 κ ) µC { µ + 2( κ − µκ )}] (1.18) 6y 11 x1 yx1 2 x 2 2yx2 1 x 12 x 22 2 2 MSE( t )=θ Y [ C + λ C ( λ −+ 2 κ ) λC { λ + 2( κ − λκ )}] (1.19) 7y 11 x1 yx1 2 x 2 2yx2 1 x 12 x 22 2 2 MSE( t )=θ Y [ C + γ C ( γ −+ 2 κ ) γC { γ + 2( κ − γκ )}] (1.20) 8y 11 x1 yx1 2 x 2 2yx2 1 x 12 x where C S Cy Cy x1 y 11 κρyx= yx , κρyx= yx , κρxx= xx , Cy = , θ = − , 11C 22C 12 12C Y nN x1 x2 x2 XC S S ixi Xxiiβ2 () Xi xi yxi λi = , γ = , µ = , C = , ρ = , XC+ β () x i Xβ () xC+ i X + ρ xi X yxi SS ixi 2 i iix2 i i xx12 i yxi N N N ()yY− 2 ()xX− 2 ∑ j ∑ ij i ∑ (yj−− Yx )( ij X i ) 2 j=1 2 j=1 2 j=1 Sy = , Sx = , S = , i =1, 2 (N − 1) i (N − 1) yxi (N − 1) Many authors, such as Kadilar and Cingi ([2], [3]), Shabbir and Gupta [13], Singh and Vishwakarma [14], Koyuncu and Kadilar ([4], [5], [6], [7]), Sanaullah et al. [8], Mouhamed et al. [9], Parmar et al. [11], Tailor et al [17], Yadav et al. ([19], 50 Subhash Kumar Yadav et al.: Improved Ratio-Cum-Product Estimators of Population Mean Using Known Population Parameters of Auxiliary Variables [20]), Onyeka et al. [10] have improved the ratio and product estimators as given in (2.1) and (2.3 for the population mean of the study variable in the stratified random sampling. 2. Proposed Estimator Making the use of auxiliary information more appropriately, assuming that the information on co-efficient of kurtosis and median of auxiliary variables x1 and x2 are known, we propose the estimator as, Xββ() x++ Mx () x ( x ) Mx ( ) ty= 12 1dd 1 22 2 2 (2.1) x12ββ() x 1++ Mxdd () 1 X 22 ( x 2 ) Mx ( 2 ) To obtain the bias and mean square error of the proposed estimator, we assume that 22 yY=(1 + e ) , xX=(1 + e ) , xX=(1 + e ) such that Ee()()()0= Ee = Ee = and Ee()= θ C , 0 11 122 2 012 0 y 2222 Ee()= θ C , Ee()= θ C , Eee()= θρ CC , Eee()= θρ CC , E() ee= θρ C C . 1 x1 2 x2 01 yx11 y x 02 yx22 y x 12 xx12 x 1 x 2 , Expressing the proposed estimator t in terms of ei s, we get X12ββ() x 1+ Mxdd () 1 X 2 (1) ++ e 2 2 ( x 2 ) Mx ( 2 ) tY=(1 + e0 ) X1(1)++ e 1ββ 2 () x 1 Mxdd () 1 X 22 ( x 2 )+ Mx ( 2 ) X12β() x 1+ Mxdd () 1 X22 ββ() x 2++ Mx () 2 X 22 () xe 2 2 =Ye(1 + 0 ) X12ββ() x 1++ Mxdd () 1 X 12 () xe 1 1 X22 β()() x 2+ Mx 2 −1 =++Ye(10 )(1ηη 11 e ) (1 + 2 e 2 ) 22 =+Ye(10 )(1 −+ηη 11 e 1 e 1)(1 + η 2 e 2 ) 22 =+Y(1 e0 )(1 −+η 11 e η 1 e 1 − ηη 1 212 ee + η 2 e 2) 22 =Y(1 +− e0η 11 e + η 11 e − ηη 1212 ee + η 22 e − η 101 ee + η 202 ee) 22 or t−= Y Y() e0 −η 11 e + η 11 e − ηη 1212 ee + η 22 e − η 101 ee + η 202 ee , (2.2) Xxiiβ2 () where ηi = i =1, 2 Xiβ2 () x i+ Mx di () Taking expectation on both sides of (2.2), we get 22 Et()−= Y YEe (0 −η 11 e + η 11 e − ηη 1212 ee + η 22 e − η 101 ee + η 202 ee) 22 =YEe[()()()()()()()]01111121222101202 −+η Ee η Ee − ηη Eee + η Ee − η Eee + η Eee 2 Substituting the values of Ee()0 , Ee()1 , Ee()2 , Ee()1 , E() ee12 , Eee()01 and Eee()02 we get the bias of t as 22 Bt()[()(=θ Y η C η −+ κ ηC η κ − ηκ )] (2.3) 11x1 yx 1 2 x 2 2 yx 2 1 x 12 x To find the mean squared error of the suggested estimator t up to first degree of approximation square and take expectation on both sides of (2.2).