scholarly journals Application of Balanced Sampling, Non-Response and Calibrated Estimator

2016 ◽  
Vol 55 (1) ◽  
pp. 81-90
Author(s):  
Ieva Dirdaitė ◽  
Danutė Krapavickaitė
Keyword(s):  
Mean Squared Error ◽  
Labour Force ◽  
Age Groups ◽  
Auxiliary Information ◽  
Cluster Sampling ◽  
Random Cluster ◽  
Balanced Sampling ◽  
Squared Error ◽  
Indicator Variables ◽  
Design Weights

The aim of this paper is to study the interplay between balanced sampling, non-response and calibratedestimator by simulation. The results of seven strategies, embracing a combination of balanced sampling via the cubemethod, simple random cluster sampling, adjustment for non-response, Horvitz–Thompson estimator of the total andcalibration of design weights, are compared. Auxiliary information is used for all strategies at least at one of the stages(sampling or estimation). This auxiliary information consists of indicator variables for sex, age groups and urban/ruralliving area, and their totals. Real Labour Force Survey data of Statistics Lithuania are used for simulation. Bias, varianceand relative mean squared error are measures of accuracy for the comparison of results.

scholarly journals Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Nelson Kiprono Bii ◽  
Christopher Ouma Onyango ◽  
John Odhiambo
Keyword(s):  
Finite Population ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Cluster Sampling ◽  
Sample Surveys ◽  
Population Mean ◽  
Second Stage ◽  
Kernel Weights ◽  
Squared Error ◽  
Interval Lengths

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

scholarly journals A Family of Difference-Cum-Exponential Type Estimators for Estimating the Population Variance Using Auxiliary Information in Sample Surveys

2014 ◽  
Vol 1 ◽  
pp. 15-21
Author(s):  
H.S. Jhajj ◽  
Kusam Lata
Keyword(s):  
Linear Regression ◽  
Exponential Type ◽  
Random Sampling ◽  
Sampling Design ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Double Sampling ◽  
Population Variance ◽  
Squared Error

Using auxiliary information, a family of difference-cum-exponential type estimators for estimating the population variance of variable under study have been proposed under double sampling design. Expressions for bias, mean squared error and its minimum values have been obtained. The comparisons have been made with the regression-type estimator by using simple random sampling at both occasions in double sampling design. It has also been shown that better estimators can be obtained from the proposed family of estimators which are more efficient than the linear regression type estimator. Results have also been illustrated numerically as well asgraphically.

Using auxiliary information to estimate stand tables

2000 ◽  
Vol 30 (6) ◽  
pp. 865-872 ◽  
Author(s):  
Edwin J Green ◽  
Michael Clutter
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Absolute Error ◽  
Bayes Estimator ◽  
Diameter Class ◽  
Usual Estimator ◽  
Squared Error ◽  
Composite Estimator ◽  
Variance Estimates

The problem of estimating stand tables in stands with few sample points is considered. The usual point-sampling estimate of trees per hectare by diameter class is examined, along with two alternative estimators: a precision-weighted composite estimator and a pseudo-Bayes estimator. A large-scale forest inventory is simulated, and stand tables are estimated for each stand with each of the three estimators. Both the composite and pseudo-Bayes estimator appear superior (in terms of mean absolute error and mean squared error) to the usual estimator. The pseudo-Bayes estimator appears to perform the best (with an 80% reduction in mean squared error). This estimator also is easier to use than the composite estimator because it does not require within diameter class variance estimates.

Prevalence and Correlates of Tobacco Smoking in Sri Lanka

2010 ◽  
Vol 23 (6) ◽  
pp. 861-869 ◽  
Author(s):  
Prasad Katulanda ◽  
Kremlin Wickramasinghe ◽  
Jayaweera G. Mahesh ◽  
Amila Rathnapala ◽  
Godwin R. Constantine ◽  
...  
Keyword(s):  
Response Rate ◽  
Middle Age ◽  
Age Groups ◽  
Cluster Sampling ◽  
Sri Lankan ◽  
Cross Sectional ◽  
Current Smoking ◽  
Random Cluster ◽  
Sociodemographic Correlates ◽  
Lower Education

Objectives: This study aimed to determine the prevalence and underlying sociodemographic correlates of smoking among Sri Lankans. Methods: A cross-sectional sample (N = 5000, age >18 years) was selected using a multistage random cluster sampling. Data were collected using an interviewer-administered questionnaire. Results: Response rate was 91% (n = 4532); males 40%; mean age 46.1 years (±15.1). Overall, urban and rural prevalence of current smoking (smoking) was 18.3%, 17.2%, and 18.5%, respectively ( P = nonsignificant, urban vs rural). Smoking was much higher in males than in females (38.0% vs 0.1%, P < .0001). Ex-smokers comprised 10.0% (males 20.7%, females 0.1%, P < .0001). Among the smokers 87.0% smoked <10 cigarettes per day. The male age groups < 20 and 20 to 29 years had the lowest (15.6%) and the highest (44.6%) prevalence of smoking, respectively. In males, smoking was highest in the least educated (odds ratio = 1.96, P = .001). Conclusions: Smoking is common among Sri Lankan males and is associated with lower education, income, and middle age.

scholarly journals ON THE DEVELOPMENT OF RATIO TYPE ESTIMATORS USING AUXILIARY INFORMATION

2021 ◽  
Vol 21 (1) ◽  
pp. 163-170
Author(s):  
MUHAMMAD IJAZ ◽  
ATTA ULLAH ◽  
TOLGA ZAMAN
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Ratio Estimator ◽  
Order Of Approximation ◽  
Large Sample ◽  
First Order ◽  
Squared Error ◽  
Ratio Estimators ◽  
Modified Forms

The paper produces some new modified forms of the ratio estimators using the auxiliary information. The large sample properties, that is, the bias and mean squared error up to the first order of approximation are determined. The comparison is made with other existing estimators by using an applied data. It has been observed that the proposed estimators have a fewer mean squared error and leads to the efficient results as compared to the classical ratio estimator, Sisodia and Dwivedi, Singh and Kakran, Upadhyaya and Singh estimators.

scholarly journals Using Auxiliary Information More Efficiently in Population Variance Estimation - A New Family of Estimators

2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Kalim Ullah ◽  
Zawar Hussain ◽  
Salman Arif Cheema
Keyword(s):  
Finite Population ◽  
Variance Estimation ◽  
Theoretical Study ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
First Order ◽  
New Family ◽  
Squared Error ◽  
Estimation Of Variance

In this article, we have suggested estimation of variance in finite population by using known values of parameter related to auxiliary information such as rank and second raw moment of auxiliary variable in stratified random sampling. The expression for the bias and mean squared error (MSE) of the suggested estimator are obtained up to first order of approximation. The proposed estimator is efficient comparatively various other estimators. A numerical and theoretical study are performed to support the suggested estimator.

scholarly journals A Generalized Two Phase Sampling Estimator of Ratio of Population Means Using Auxiliary Information

2021 ◽  
Author(s):  
Tarunpreet Kaur Ahuja ◽  
Peeyush Misra ◽  
O. K. Belwal
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Two Phase ◽  
Population Means ◽  
Minimum Mean Squared Error ◽  
Second Moments ◽  
Squared Error ◽  
Phase Sampling ◽  
Theoretical Results ◽  
Two Phase Sampling

This paper addresses the problem of estimating ratio of two population means by using quantitative auxiliary knowledge in the form of first and second moments. Through this paper, an improved generalized two phase sampling estimator has been proposed. The relative bias and mean squared error of the suggested estimator has been derived and studied. Also, a comparative study with the conventional estimators has been included to establish its superiority. Besides theoretical comparisons, a subset of optimum estimators having the same minimum mean squared error (MSE) is also explored. An empirical study is also carried out to support theoretical results.

scholarly journals A General Class of Dual to Ratio Estimator

2020 ◽  
pp. 421-431
Author(s):  
Housila Prasad Singh ◽  
Pragati Nigam
Keyword(s):  
General Class ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Ratio Estimator ◽  
Sample Surveys ◽  
Sample Mean ◽  
Population Mean ◽  
Squared Error ◽  
Class Of Estimators ◽  
Ratio Estimators

In this paper we have considered the problem of estimating the population mean using auxiliary information in sample surveys. A class of dual to ratio estimators has been defined. Exact expressions for bias and mean squared error of the suggested class of dual to ratio estimator have been obtained. In particular, properties of some members of the proposed class of dual to ratio estimators have been discussed. It has been shown that the proposed class of estimators is more efficient than the sample mean, ratio estimator, dual to ratio estimator and some members of the suggested class of estimators in some realistic conditions. Some numerical illustrations are given in support of the present study.

scholarly journals Efficient transformed ratio-type estimator using single auxiliary information

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Sana Amjad ◽  
◽  
Muhammad Ismail ◽  
Keyword(s):  
Mean Squared Error ◽  
Order Approximation ◽  
Real Life ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Variance Estimators ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data ◽  
Better Than

This paper provides an efficient transformed ratio-type estimator to estimate the study variable's population variance by utilizing information of a single auxiliary variable under simple random sampling without replacement. The bias and mean squared error of the proposed estimator are derived up-to 1st order approximation. In addition to this, the efficiency comparison of the proposed estimator has been done with traditional ratio-type variance estimator and some other widely used modified ratio-type variance estimators by taking real-life data. A simulation study has also been carried out to see the performance of the proposed estimator. It is worth noticing that our proposed estimator performs better than the competing estimators in real-life data applications as the mean squared error and root mean squared error of our proposed estimator are smaller than the competing estimators. Hence, our proposed estimator is better than existing variance estimators.

scholarly journals Some Improved Estimators of Population Mean Under Systematic Sampling

2021 ◽  
Vol 3 (1) ◽  
pp. 15-27
Author(s):  
Shagufta Mehnaz ◽  
Shakeel Ahmed
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Degree Of Approximation ◽  
Systematic Sampling ◽  
Population Parameters ◽  
Auxiliary Variables ◽  
Population Mean ◽  
Sampling Schemes ◽  
Squared Error ◽  
Improved Estimators

Auxiliary information is very important in constructing estimators for the population parameters for increasing the efficiency different sampling schemes. In this paper, we consider the problem of estimation of population mean using information on auxiliary variables in systematic sampling. We derive the expressions for the bias and mean squared error (MSE) of the suggested estimators up to the 1st degree of approximation. Proposed estimators are compared with usual mean estimator in systematic sampling scheme theoretically as well as empirically.

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