Medical, Pharma, Engineering, Science, Technology and Business

Department of Mathematics and Statistics, University of Uyo. P.M.B 1017, Uyo. Nigeria

- *Corresponding Author:
- Anieting AE

Department of Mathematics and Statistics

University of Uyo. P.M.B 1017, Uyo. Nigeria

**E-mail:**[email protected]

**Received date:** April 21, 2015; **Accepted date:** May 22, 2015; **Published date:** May 29, 2015

**Citation:** Anieting AE (2015) Comparison of Horvitz and Thompson Estimator with that of Rao, Hartley and Cochran Estimator in PPS without Replacement Scheme. J Biom Biostat 6:222. doi: 10.4172/2155-6180.1000222

**Copyright:** © 2015 Anieting AE. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.

**Visit for more related articles at** Journal of Biometrics & Biostatistics

This paper was focused on the comparison of Horvitz and Thompson estimator of population total with that of Rao, Hartley and Cochran estimator in PPS without replacement scheme when a sample of size six is taken from the same finite population. The data used were from Nigerian Bureau of Statistics bulletin. The result showed that the variances of both estimators gave positive values but the variance of the estimator by Rao, Hartley and Cochran was smaller making it a better estimator.

PPS without replacement; Rao, Hartley and Cochran estimator; Horvitz and Thompson estimator

Unequal probability sampling scheme or proportional to size (PPS) sampling scheme ensures that the possibility of selecting each unit into the sample is in proportion to the size or measure of size of the unit provided that the sizes of the individual units in the population are known and are correlated with the variable of interest, Horvitz and Thompson (1952) were the first to give theoretical frame work of PPS without replacement [1]. The sample variance of the population total estimator given by Horvitz and Thompson (1952) has the possibility of giving negative values for some samples. This problem has made statistician to work on this scheme. They include Yates and Grundy(1953), Sen (1953), Durbin(1953), Rao, Hartley and Cochran (1962) etc. Rao, Hartley and Cochran (1962) gave a modified estimator based on PPS without replacement scheme. The variance of the estimator for population total under Rao, Hartley and Cochran is always positive. But how do the estimators given by Horvitz and Thompson , and that given by Rao,Hartley and Cochran fare when the sample size is more than two, say six, assuming that the variance of the population total estimator by Horvitz and Thompson gives a positive value. The data for empirical verification of this study is from Nigerian Bureau of Statistics bulletin [1].

**The two procedures**

**Horvitz and thompson estimation (procedure): **First, the cumulative totals and ranges of the measure of size are formed [2]. Then to select a sample of size n without replacement, a random start between 1 and k inclusive (K=X/n) is selected using a table of random numbers. If the number selected is r then the units in the sample are those in whose ranges the umbers r,r+k,r+2k,….,r+(n-1)k fall. The probability of selecting the unit Ui in the sample of size n is

If any unit in the population has its size greater than or equal to k such unit is removed before sampling and taken into the sample with probability unity. The probability that any pair of units (U_{i}, U_{j}) is together in the sample is

Where q_{ij} is the number of the random numbers between 1 and k inclusive, which will select U_{i} and U_{j} simultaneously in the sample?

An unbiased estimator of the population total for PPS without replacement as given by Horvitz and Thompson is

While the sample estimator of is given as

**Rao, hartley and cochran estimation (procedure): **Divide a population of N units into n groups at random with group g containing N_{g} units ( g=1,2,….,n) such that N_{1}+…. + N_{n} = N. Thereafter select one unit independently from each group [3]. This will give a total of n units selected in the sample with PPS without replacement. The probability of selecting U_{i} in the sample in gth group is

X_{i}’s are the auxiliary variables and P_{g} is the sum of the initial probabilities in g^{th} group.

The Rao, Hartley and Cochran estimator of the population total is

Where y_{gi} is the value of the study variate for the i^{th} unit in g^{th} group and

The estimator of the variance is given as

The correlation coefficient r between the auxiliary variable x_{i} (the number of local government in each state) and the variable of interest y_{i} (the number of police stations in Nigeria in each state) is 0.60. Other results of the analysis are summarized in **Tables 1-3**.

S/N | States | X_{i} |
Range | Y_{i} |
---|---|---|---|---|

1 | Abia | 18 | 1-18 | 22 |

2 | Adamawa | 21 | 19-39 | 41 |

3 | AkwaIbom | 31 | 40-70 | 65 |

4 | Anambra | 20 | 71-91 | 52 |

5 | Bauchi | 8 | 92-111 | 38 |

6 | Bayelsa | 23 | 112-119 | 37 |

7 | Benue | 27 | 120-142 | 27 |

8 | Borno | 18 | 143-169 | 42 |

9 | Cross River | 25 | 170-187 | 25 |

10 | Delta | 12 | 188-212 | 47 |

11 | Ebonyi | 18 | 213-224 | 29 |

12 | Edo | 16 | 225-242 | 31 |

13 | Ekiti | 16 | 243-258 | 21 |

14 | Enugu | 11 | 259-274 | 21 |

15 | Gombe | 27 | 275-285 | 14 |

16 | Imo | 27 | 286-312 | 63 |

17 | Jigawa | 27 | 313-339 | 65 |

18 | Kaduna | 23 | 340-362 | 59 |

19 | Kano | 45 | 363-407 | 123 |

20 | Katsina | 34 | 408-442 | 38 |

21 | Kebbi | 20 | 443-461 | 9 |

22 | Kogi | 21 | 462-482 | 40 |

23 | Kwara | 16 | 483-498 | 7 |

24 | Lagos | 20 | 499-518 | 43 |

25 | Nassarawa | 13 | 519-531 | 60 |

26 | Niger | 26 | 532-557 | 39 |

27 | Ogun | 20 | 558-577 | 25 |

28 | Ondo | 18 | 578-595 | 36 |

29 | Osun | 30 | 596-625 | 50 |

30 | Oyo | 33 | 626-658 | 47 |

31 | Plateau | 17 | 659-675 | 20 |

32 | Rivers | 23 | 676-698 | 34 |

33 | Sokoto | 23 | 699-721 | 3 |

34 | Taraba | 16 | 722-737 | 71 |

35 | Yobe | 17 | 738-754 | 19 |

36 | Zamfara | 14 | 755-768 | 35 |

**Table 1: **The correlation coefficient Are between the auxiliary variable x_{i} and the variable of interest y_{i}.

s/n | sample | π_{i} |
y_{i} |
y_{i}/π_{i} |
---|---|---|---|---|

1 | Bauchi | 20/128 | 38 | 243.2 |

2 | Edo | 18/128 | 31 | 220.4 |

3 | Kaduna | 23/128 | 59 | 328.3 |

4 | Kwara | 16/128 | 7 | 56 |

5 | Osun | 30/128 | 50 | 213.3 |

6 | Yobe | 17/128 | 19 | 143.1 |

1204.3 |

**Table 2: **The population total estimate using Horvitz and Thompson estimator.

Selected states | P^{*}_{i} |
P_{i} |
P_{g} |
y_{gi} |
y_{gi}/P^{*}_{i} |
---|---|---|---|---|---|

Bornu | 27/137 | 27/768 | 137/768 | 42 | 213.11 |

Benue | 23/126 | 23/768 | 126/768 | 27 | 147.91 |

Edo | 18/133 | 18/768 | 133/768 | 31 | 229.06 |

Taraba | 16/101 | 16/768 | 101/768 | 71 | 448.19 |

Delta | 25/113 | 25/768 | 113/768 | 47 | 212.44 |

Ogun | 20/158 | 20/768 | 158/768 | 25 | 197.5 |

1448.21 |

**Table 3: **Using eqn 2.1.1., the variance gives 690317.62 also using 2.2.1 the
variance gives 102892.4.

Based on a sample size of six from a population of 36 units, **Tables 2 and 3** show that the population total estimate using Horvitz and Thompson estimator is 1204 which is smaller than 1448, the population total estimate using Rao,Hartley and Cochran estimator. Also the variance of the estimate of the population total by Horvitz and Thompson is 690317.62 which is bigger than 102892.4, the variance of the estimate of the population total by Rao, Hartley and Cochran [4].

Since the variance of the estimator given by Rao, Hartley and Cochran gives a value smaller than that given by Horvitz and Thompson with respect to sample size of six,it shows that the estimator given by Rao,Hartley and Cochran is better than that given by Horvitz and Thompson.

- AlodatNA (2009) on unequal probability Sampling Without Replacement Sample Size 2. IntJ Open Problems Comp Maths2:1108-112.
- Nigerian Bureau of Statistics bulletin(2008 Edition)
- DawoduOO, AdewareAA,Oshundare IO (2013) Rao, Hartley and Cochran’s Sampling Scheme:Application. Mathematical Theory and Modeling 3:8 101- 103.
- OkaforFC (2013). Sample Survey Theory with Applications. Afro- Orbis Publication Ltd,2002. Open Journal of Statistics.

Select your language of interest to view the total content in your interested language

- Adomian Decomposition Method
- Algebra
- Algebraic Geometry
- Algorithm
- Analytical Geometry
- Applied Mathematics
- Artificial Intelligence Studies
- Axioms
- Balance Law
- Behaviometrics
- Big Data Analytics
- Big data
- Binary and Non-normal Continuous Data
- Binomial Regression
- Bioinformatics Modeling
- Biometrics
- Biostatistics methods
- Biostatistics: Current Trends
- Clinical Trail
- Cloud Computation
- Combinatorics
- Complex Analysis
- Computational Model
- Computational Sciences
- Computer Science
- Computer-aided design (CAD)
- Convection Diffusion Equations
- Cross-Covariance and Cross-Correlation
- Data Mining Current Research
- Deformations Theory
- Differential Equations
- Differential Transform Method
- Findings on Machine Learning
- Fourier Analysis
- Fuzzy Boundary Value
- Fuzzy Environments
- Fuzzy Quasi-Metric Space
- Genetic Linkage
- Geometry
- Hamilton Mechanics
- Harmonic Analysis
- Homological Algebra
- Homotopical Algebra
- Hypothesis Testing
- Integrated Analysis
- Integration
- Large-scale Survey Data
- Latin Squares
- Lie Algebra
- Lie Superalgebra
- Lie Theory
- Lie Triple Systems
- Loop Algebra
- Mathematical Modeling
- Matrix
- Microarray Studies
- Mixed Initial-boundary Value
- Molecular Modelling
- Multivariate-Normal Model
- Neural Network
- Noether's theorem
- Non rigid Image Registration
- Nonlinear Differential Equations
- Number Theory
- Numerical Solutions
- Operad Theory
- Physical Mathematics
- Quantum Group
- Quantum Mechanics
- Quantum electrodynamics
- Quasi-Group
- Quasilinear Hyperbolic Systems
- Regressions
- Relativity
- Representation theory
- Riemannian Geometry
- Robotics Research
- Robust Method
- Semi Analytical-Solution
- Sensitivity Analysis
- Smooth Complexities
- Soft Computing
- Soft biometrics
- Spatial Gaussian Markov Random Fields
- Statistical Methods
- Studies on Computational Biology
- Super Algebras
- Symmetric Spaces
- Systems Biology
- Theoretical Physics
- Theory of Mathematical Modeling
- Three Dimensional Steady State
- Topologies
- Topology
- mirror symmetry
- vector bundle

- 7th International Conference on
**Biostatistics**and**Bioinformatics**

September 26-27, 2018 Chicago, USA - Conference on
**Biostatistics****and****Informatics**

December 05-06-2018 Dubai, UAE

- Total views:
**11812** - [From(publication date):

April-2015 - Mar 22, 2018] - Breakdown by view type
- HTML page views :
**8016** - PDF downloads :
**3796**

Peer Reviewed Journals

International Conferences
2018-19