Calibrating Design Weights in Stratified Sampling Using Auxiliary Information on Coefficient of Variation

2018 ◽  
Author(s):  
Dinesh K. Rao ◽  
Tokaua B. Tekabu
Keyword(s):  
Coefficient Of Variation ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Design Weights

scholarly journals Memory Type Ratio and Product Estimators in Stratified Sampling

2020 ◽  
Author(s):  
Irfan Aslam ◽  
Muhammad Noor-ul-Amin ◽  
Uzma Yasmeen ◽  
Muhammad Hanif
Keyword(s):  
Simulation Study ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Real Life Data ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
Mean Square Errors ◽  
Weighted Moving Average

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

scholarly journals The Estimation of Finite Population Variance Under Stratified Sampling Technique

2021 ◽  
Author(s):  
Uzma Yasmeen ◽  
Muhammad Noor-ul-Amin
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Variance Estimators ◽  
Usual Estimator ◽  
Study Population

The efficiency of the study variable can be improved by incorporating the information from the known auxiliary variables. Usually two techniques ratio and regression estimation are used with the help of auxiliary information in different approaches to acquire the high precision of the estimators. Considering the very heterogeneous population to get the size of the sample it may be originating impossible to get a sufficiently accurate and precise estimate by taking the simple random sampling technique from the complete population. Occasionally taking sample issue may differ significantly in different part of the entire population. For example, under study population consists of people living in apartments, own homes, hospitals and prisons or people living in plain regions and hill regions so in such situations the stratified sampling is one of the most commonly used approach to get a representative sample in survey sampling from different cross units of the population. The present study is set out on the recommendation of generalized variance estimators for finite population variance incorporating stratified sampling scheme with the information of single and two transformed auxiliary variables. The expressions of bias and mean square error (MSE) are obtained for the advised exponential type estimators. The conditions are obtained for which the anticipated estimators are better than the usual estimator. An empirical and simulation study is conducted to prove the superiority of the recommended estimator.

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 Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information

Silva Fennica ◽  
2020 ◽  
Vol 54 (2) ◽  
Author(s):  
Agnese Marcelli ◽  
Walter Mattioli ◽  
Nicola Puletti ◽  
Francesco Chianucci ◽  
Damiano Gianelle ◽  
...  
Keyword(s):  
Large Scale ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Monte Carlo Integration ◽  
Regression Estimator ◽  
Two Phase ◽  
Wood Volume ◽  
Poplar Plantations ◽  
Sentinel 2 ◽  
Tessellation Stratified Sampling

Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed.

scholarly journals Spatially stratified sampling using auxiliary information for geostatistical mapping

2009 ◽  
Vol 18 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Matthew G. Falk ◽  
Robert J. Denham ◽  
Kerrie L. Mengersen
Keyword(s):  
Auxiliary Information ◽  
Stratified Sampling
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