**Open Access**-
**Authors :**Alok Kumar Shukla -
**Paper ID :**IJERTV8IS120345 -
**Volume & Issue :**Volume 08, Issue 12 (December 2019) -
**Published (First Online):**02-01-2020 -
**ISSN (Online) :**2278-0181 -
**Publisher Name :**IJERT -
**License:**This work is licensed under a Creative Commons Attribution 4.0 International License

#### Efficient Classes of Modified Ratio and Product Estimators for Estimating Population Mean under Two-Phase Sampling

Alok Kumar Shukla

Department of Statistics,

D.A-V. College, Kanpur-208001, U.P., INDIA

Abstract:- In this Scripture, a ratio and product type exponential estimators have been suggested for improved estimation of population mean in two-phase random sampling. The biases and the mean squared errors (MSE) of the suggested estimators are derived up to the first order of approximation. The optimum values of the characterizing scalars are also obtained. The minimum values of the MSEs of the suggested estimators are obtained for these optimum values of the characterizing constants. A theoretical comparison has been made with the existing competing estimators and the efficiency conditions are verified using two natural populations.

Keywords- Ratio estimator, Product estimator, Efficiency, Mean squared errors

INTRODUCTION

The most commensurate estimator for estimating the population mean is the sample mean that is the corresponding statistic. Unfortunately the sampling distribution of sample mean is widely dispersed round the true population mean. Thus its sampling variance is reasonably large. Thus we search for even biased estimator but having sampling distribution close to population mean. This is achieved through the use of auxiliary variable, which is highly positively or negatively associated with the main variable under study. Ratio and product method of estimation are applied when main and auxiliary variables are highly positively and negatively correlated respectively. Many times we come across the problems when the

population mean of the auxiliary variable is not known. In such situation, two-phase sampling is used when a lager sample is drawn to estimate the population mean of the auxiliary variable and then a small sample of appropriate size is drawn to estimate the population mean of the study variable.

Let the population under consideration consists of N distinct and identifiable units. A sample of size n1 is drawn without replacement to estimate the population mean of the auxiliary variable and then a sub-sample of size n is drawn without replacement form n1 units in two ways:

Case I: When the second phase sample of size n is a

subsample of the first phase of size n1 .

Case II: When the second phase sample of size n is drawn independently of the first phase sample of size n1 .

REVIEW OF EXISTING ESTIMATORS Various authors used auxiliary information and suggested ratio and product type estimators for enhanced estimation of population mean in two-phase random sampling. The following Table-1 represents various estimators of population mean under two-phase random sampling for both the cases along with their mean squared error up to the first order of approximation.

Table-1: Various estimators and their MSE for two different cases

S. No.

Estimator

MSE for Case-I

MSE for Case-II

1.

1 n

y yi

n i1

Sample mean estimator

Y 2 1 f C 2

n y

Y 2 1 f C 2

n y

2.

yd R

Usual Ratio Estimator

1 f 1 f *

Y 2 C 2 C 2 (1 2C)

n y n x

Y 2 1 f C 2 f **C 2 21 f CC 2

n y x n x

3.

y*d R

Kumar ans Bahl (2006) Estimator

1 f 1 f *

Y 2 C 2 g' C 2 (g'2C)

n y n x

Y 2 1 f C 2 g' C 2 ** 1 f

n y x g' f 2C n

4.

y d

Re

Singh and

Vishwakarma (2007) Estimator

1 f 1 f * 1

Y 2 C 2 C 2 C

n y n x 4

Y 2 1 f C 2 1 f **C 2 1 f CC 2

n y 4 x n x

5.

y *d Re

Kalita and Singh (2013) Estimator

1 f 1 f * 1

Y 2 C 2 g' C 2 g'C

n y n x 4

Y 2 1 f C 2 1 g'2 f **C 2 g' 1 f CC 2

n y 4 x n x

6.

yd P

Usual Product Estimator

1 f 1 f *

Y 2 C 2 C 2 (1 2C)

n y n x

Y 2 1 f C 2 f **C 2 21 f CC 2

n y x n x

7.

y*d P

Singh and Chaudhury (2012) Estimator

1 f 1 f *

Y 2 C2 g' C2 (g'2C)

n y n x

Y 2 1 f C 2 g' C 2 ** 1 f

n y x g' f 2C n

8.

y d Pe

Singh and

Vishwakarma (2007) Estimator

1 f 1 f * 1

Y 2 C 2 C 2 C

n y n x 4

Y 2 1 f C 2 1 f **C 2 1 f CC 2

n y 4 x n x

9.

y *d Pe

Kalita and Singh (2013) Estimator

1 f 1 f * 1

Y 2 C 2 g' C 2 g'C

n y n x 4

Y 2 1 f C2 1 g'2 f **C2 g' 1 f CC 2

n y 4 x n x

Where,

i

i

i

i

1

1

2 1 N 2 2 N 2

1 N 1 N

Sy ( y Y ) ,

N 1 i1

Sx (x X ) ,

N 1 i1

S yx ( yi Y )(xi X ) ,

N 1

N 1

i1

X X i ,

N

N

i1

N

N

Y 1 N Y

,

,

C Sy ,

C Sx ,

C Cy ,

S yx ,

f n ,

f n1 ,

f * n ,

1

1

C

C

f **

i1

1 f

i y

1 f1 ,

Y x X

g' n .

yx

x

yx S

y Sx

N 1 N n

n n1 n1 n

PROPOSED ESTIMATORS

Under this section, an exponential dual to ratio and product-type estimators, respectively, are suggested as follows:

x*d x

Re y 1 exp 1

(1)

1

1

x*d x

x x *d

P e y 1 exp 1

*d

*d

(2)

x1 x

Where, is a characterizing scalar and is obtained by minimizing the mean squared error of the proposed estimators. The Bias and MSE of the proposed estimators are obtained for the following two cases.

Case I:

To study the large sample properties of the proposed class of estimators, we define

y Y 1 e0 , x X 1 e1 and x1 X 1 e2 such that E(e0 ) E(e1 ) E(e2 ) 0

and

E(e2 ) C 2 , E(e2 ) C 2 , E(e2 ) * C 2 , E(e e ) CC 2 , E(e e ) * CC 2 E(e e

) * C ,

0 y 1 x 2 x 0 1 x 0 2

1 1 * 1 1 ** 1 1 Cy n

x 1 2 x

,

n N

n

,

N

n n

, C yx C

and g

n n

1

1 x 1

Using above up to the first order of approximation,, the proposed estimator may be written as,

Y 1 e 1 1 1 g e

e e2 e e 1 g 2 e e

e2 e2 1 g 2 e2 e2 (3)

Re 0

2 2 1 2 2 1 4

1 2 2 1 8

2 1

Thus we have bias of the proposd estimator as

B

Y 1 g2 *C2 1 g2 C2 1 g **CC2 1

(4)

Re 8

x 8 x 2

x

And MSE of the proposed estimator as,

2 * 2 1

2

2 2 * 2

MSE Re

Cy g

Y 2

Cx

4

g C g

4

Cx

Cx

(5)

g 2 C2 *C2 g *CC2 CC 2

2

x x x x

The optimum value of the characterizing scalar is obtained by minimizing MSE in (5) using the method of maxima-minima as,

x x x x x x

x x x x x x

g C 2 *C 2 g C 2 *C 2 2 *CC 2 CC 2 0

x x x x

x x x x

g C 2 *C 2 2 *CC 2 CC 2 A

x x

x x

g C 2 *C 2 gB

(6)

Where

A g C 2 *C 2 2 *CC 2 CC 2 and B C 2 *C 2

x x x x

x x x x

x x

x x

The minimum value of bias of the proposed estimator is obtained by putting optimum value of in (4) as,

1 2 * 2

1 2 2 1

** 2 A

B Re Y 8 g

Cx 8 g Cx 2 g

CCx 1 gB

(7)

Minimum value of the MSE of the proposed estimator is obtained by putting the optimum value of in (5) and thus the

2 2 * 2 1 A2

minimum MSE is given as, MSE Re Y Cy g Cx 4 g C 4B

(8)

Similarly, Bias and MSE of proposed product estimator in equation (2)

The minimum value of bias of the proposed estimator is obtained by putting optimum value of as,

3 2 2 1 2 * 2 1

** 2 A

B P e Y 8 g Cx 8 g

Cx 2 g

CCx 1 gB

(9)

Minimum value of the MSE of the proposed estimator is obtained by putting the optimum value of and thus the minimum

2 2 * 2 1

A2

MSE is given as, MSE P e Y

Cy g Cx 4 g C 4B

(10)

Where

A g C 2 *C 2 2 CC 2 *CC 2 and B C 2 *C 2

Case II

x x x x x x

To study the large sample properties of the proposed class of estimator, we define

y Y 1 e0 , x X 1 e1 and x1 X 1 e2 such that

E(e0 ) E(e1 ) E(e2 ) 0

and

E(e2 ) C 2 , E(e2 ) C 2 , E(e2 ) * C 2 , E(e e )

CC 2 , E(e e ) 0 , E(e e ) 0 ,

1

1 ,

0

* 1

y 1

1 **

x

1 1

2 x 0 1 x 0 2 1 2

n

n

N

N

*** * Cy n

n

,

N

n n

,

C yx

and g

n n

C

C

1 1 x 1

Similarly, Bias and MSE of proposed estimators in equation (1) and (2)

The minimum value of bias and MSE of the proposed estimator is obtained by putting optimum value of as,

1 2 *** 2 1 2 *

A

B R e Y 8 g Cx 2 gCx

C 1

gB

(11)

2 2 1 2 *** 2 2

A2

MSE R e Y Cy 4 g Cx g CCx 4B

(12)

Where

A g C 2 *C 2 2 CC 2 and B C 2 *C 2

x x x

x x x

x x

x x

Minimum value of bias and MSE of the proposed estimator is obtained by putting the optimum value of and thus the minimum MSE is given as,

3 2 *** 1 2 *

A

B P e Y 8 g

gC

2 x

C 1

gB

(13)

2 2 1 2 *** 2 2

A2

MSE P e Y Cy 4 g

Cx g CCx 4B

(14)

Where

x x x

x x x

x x

x x

A g C 2 *C 2 2 CC 2 and B C 2 *C 2

EFFICIENCY COMPARISON

A theoretical comparison of various estimators with the suggested estimator has been made and presented in Table-2. The efficiency conditions for both the cases under which the suggested estimator performs better than the competing one are also presented in this table.

Table-2: Efficiency comparison of proposed estimators with competing estimators

S. No.

Estimator

Efficiency condition for Case-I

Efficiency condition for Case-II

1.

y

V ( y) MSE( Re )I 0

V ( y) MSE( Re )II 0

2.

yd R

V ( yd ) MSE( ) 0

R Re I

V ( yd ) MSE( ) 0

R Re II

3.

y*d R

V ( y*d ) MSE( ) 0

R Re I

V ( y*d ) MSE( ) 0

R Re II

4.

y d

Re

V ( yd ) MSE( ) 0

Re Re I

V ( yd ) MSE( ) 0

Re Re II

5.

y *d Re

V ( y*d ) MSE( ) 0

Re Re I

V ( y*d ) MSE( ) 0

Re Re II

6.

yd P

V ( yd ) MSE( ) 0

P Pe I

V ( yd ) MSE( ) 0

P Pe II

7.

y*d P

V ( y*d ) MSE( ) 0

P Pe I

V ( y*d ) MSE( ) 0

P Pe II

8.

y d Pe

V ( yd ) MSE( ) 0

Pe Pe I

V ( yd ) MSE( ) 0

Pe Pe II

9.

y *d Pe

V ( y*d ) MSE( ) 0

Pe Pe I

V ( y*d ) MSE( ) 0

Pe Pe II

EMPIRICAL STUDY

Under this section, the theoretical efficiency conditions are verified using following two natural populations. The sources, descriptions and the parameters of these two natural populations are given below.

Population I: Source :Murthy (1967)

Y Output, X Number of workers

N 80, n 16, n1 30, Y 5182.64, yx 0.9150, Cy 0.3542, Cx 0.9484

Population II: Source: Kadilar and Cingi, 2006c

N 200, n 50, n1 175, Y 500, yx 0.90, Cy 25, Cx 2

Population III:- Source: Johnston 1972

Y Mean January temperature, X Date of flowering of a particular summer species (number of days from January 1)

N 10, n 2, n1 5, Y 42, yx -0.73, Cy 0.1303, Cx 0.0458

Population IV:- Source: Johnston 1972

Y Percentage of hives affected by disease, X Date of flowering of a particular summer species (number of days from January 1)

N 10, n 2, n1 5, Y 52, yx -0.94, Cy 0.1562, Cx 0.0458

The MSE of suggested ratio estimator and various competing ratio estimators for both the cases are presented in Table-3.

Table-3: Mean squared error of proposed and competing ratio estimators

Estimators

Case I

Case II

Population I

Population II

Population I

Population II

y

168488.1

2343750

168488.1

2343750

yd R

391542.8

2036607

1054187

2021946

y*d R

538452.2

2217464

1460110

2211264

y d

Re

103853.5

2186607

183515.9

2178929

y *d Re

123381.2

2280036

255511.2

226879

Re

86201.6

535714.3

68914.6

531605.1

The MSE of suggested product estimator and various competing product estimators for both the cases are presented in Table-4.

Table-4: Mean squared error of proposed and competing product estimators

Estimators

Case I

Case II

Population III

Population IV

Population III

Population IV

y

11.9794

26.3893

11.9794

26.3893

yd P

8.4789

17.1808

7.6820

14.6784

y*d P

9.3991

19.8722

8.7034

17.9519

y d Pe

9.9518

21.3597

9.3683

19.8249

y *d Pe

10.5661

22.9417

10.1360

21.8555

Pe

7.1917

8.9011

6.8725

7.7352

RESULTS AND CONCLUSION

In this manuscript, an exponential ratio and product type estimators have been suggested for improved estimation of population mean under two-phase random sampling scheme. The biases and the MSEs of the suggested estimators are derived up to the first order of approximation. The optimum values of the characterizing scalars are also obtained and the minimum MSEs of the proposed estimators for these characterizing scalars are obtained. A theoretical comparison has been made with the existing competing estimators and the conditions under which suggested estimators are better than the competing estimators are obtained. These efficiency conditions are verified using two natural populations. Thus it is recommended that the suggested estimators should be used for improved estimation of population mean under double sampling scheme.

REFERENCES

Johnston, J. (1972): Econometric methods, (2nd ed), McGraw- Hill, Tokyo.

Kadilar, C., Cingi, H. (2006c). New ratio estimators using correlation coefficient. Interstatistics 4:111.

Kalita, D., Singh, B.K. (2013). Exponential dual to ratio and dual to product-type estimators for finite population mean in double sampling. Elixir Statistics 59: 15458-15470.

Kumar, M., and Bhal, S. (2006). Class of dual to ratio estimators for double sampling, Statistical Papers, 47, 319-326

Murthy, M. N. (1967). Sampling theory and methods. Statistical Publishing Society: Calcutta, India.

Singh, B.K., Choudhury, S. (2012). Dual to product estimator for estimating population mean in double sampling, Int. J. Stat. Syst,7, 31-39.

Singh, H. P., Vishwakarma, G. K. (2007). Modified exponential ratio and product estimators for finite population mean in double sampling. Austrian Journal of Statistics, 36, 3, 217-225.