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http://krishi.icar.gov.in/jspui/handle/123456789/79677
Title: | Bootstrap Variance Estimation of Spatially Integrated Estimator of Finite Population Total in Presence of Missing Observations |
Other Titles: | Not Available |
Authors: | N.C. Paul Anil Rai Tauqueer Ahmad Ankur Biswas Prachi Misra Sahoo |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::Indian Agricultural Statistics Research Institute |
Published/ Complete Date: | 2022-09-30 |
Project Code: | Not Available |
Keywords: | Geographically weighted regression, Proportional spatial bootstrap, Spatially integrated estimator, Spatial imputation, Spatial simulation |
Publisher: | Journal of Community Mobilization and Sustainable Development |
Citation: | Paul, N.C., Rai, A., Ahmad, T., Biswas, A.* and Sahoo, P.M. (2022). Bootstrap Variance Estimation of Spatially Integrated Estimator of Finite Population Total in Presence of Missing Observations. Journal of Community Mobilization and Sustainable Development, 17(3), 1039-1048. |
Series/Report no.: | Not Available; |
Abstract/Description: | Large scale surveys, for example, household surveys, are the most important components in every national statistics system. These types of large-scale surveys are the primary and sometimes unique source of data for measuring many of the variables relating to Sustainable Development Goals (SDG) indicators. The problem of missing observations is very common in large-scale surveys. Missing data occur in surveys when an element of the target population is not observed/included in the sampling frame of the survey. This seriously affects not only the accuracy of the estimates but also the reliability of the estimates of population parameters. Imputation is a very popular method for dealing with the problem of missing data. In this article, a Proportional Spatial Bootstrap (PSB) variance estimation method for the Spatially Integrated (SI) estimator of finite population total in the presence of missing observations has been proposed utilizing various spatial imputation procedures to impute missing observations in the observed sample. The statistical properties of different spatial imputation techniques under the proposed PSB method of variance estimation were studied empirically through a spatial simulation study. The empirical results reveals that the proposed PSB method is quite efficient for variance estimation while dealing with missing observations. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Journal |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | Journal of Community Mobilization and Sustainable Development |
Journal Type: | Included NAAS journal list |
NAAS Rating: | 5.67 |
Volume No.: | 17(3) |
Page Number: | 1039-1048 |
Name of the Division/Regional Station: | Division of Sample Surveys |
Source, DOI or any other URL: | Not Available |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/79677 |
Appears in Collections: | AEdu-IASRI-Publication |
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