A STUDY OF SPATIAL BOOTSTRAP TECHNIQUES FOR VARIANCE ESTIMATION IN FINITE POPULATION

Simple item page
dc.contributor.advisorAnil Rai)
dc.contributor.authorAnkur Biswas
dc.date.accessioned2017-06-16T04:50:38Z
dc.date.available2017-06-16T04:50:38Z
dc.date.issued2014
dc.descriptiont-9085en_US
dc.description.abstractIn agricultural surveys, the important parameters of crop production are often spatial in nature, in which observations from neighbouring units tend to share similar statistical properties. In literature, spatial sampling designs were suggested to provide reliable spatial statistics using the spatial dependency among the sampling units. In this dissertation, a new efficient approach, viz. Spatial Estimation procedure, for estimation of the mean of spatially correlated finite population units was developed by incorporating the spatial dependency at the estimation stages of traditional without replacement sampling designs like Simple Random Sampling (SRS) and Ranked Set Sampling (RSS). In this approach, Spatial Estimators (SE) of population mean were developed following prediction approach in which unobserved population units were predicted based on their distances with observed sampling units following Inverse Distance Weighting (IDW) method. Since the proposed SE’s were non-linear in nature, Rescaled Spatial Bootstrap (RSB) techniques were developed for unbiased estimation of variance of the SE under both the designs. Also, a spatial simulation study was carried out in order to study the performance of proposed SE with respect to the corresponding classical estimators of population mean under both the designs along with its variance estimation following proposed RSB methods. Proposed SE under both designs were found to be almost unbiased, consistent, stable and more efficient for estimation of population mean. Further, in the context of without replacement sampling both by SRS as well as RSS designs for spatially correlated finite population, naive bootstrap approach results in considerably high amount of bias in the estimator of variance of the SE of population mean, whereas, the proposed RSB methods were found to be approximately unbiased. Further, the statistical properties of these RSB methods were not desirable in presence of missing observations. Thus, Proportional Spatial Bootstrap (PSB) methods were proposed using spatial imputation techniques for unbiased variance estimation of Spatial Estimator (SE) under SRS without replacement design. This dissertation also includes efficient rescaling Jackknife and Bootstrap methods for unbiased estimation of variance of the RSS mean estimator in case of finite population.en_US
dc.identifier.urihttp://krishikosh.egranth.ac.in/handle/1/5810020737
dc.keywordsA STUDY OF SPATIAL BOOTSTRAP TECHNIQUES FOR VARIANCE ESTIMATION IN FINITE POPULATIONen_US
dc.language.isoen_USen_US
dc.publisherINDIAN AGRICULTURAL STATISTICS RESEARCH INSTITUTE INDIAN AGRICULTURAL RESEARCH INSTITUTE PUSA, NEW DELHIen_US
dc.subAgricultural Statistics and Informaticsen_US
dc.subjectnullen_US
dc.themeA STUDY OF SPATIAL BOOTSTRAP TECHNIQUES FOR VARIANCE ESTIMATION IN FINITE POPULATIONen_US
dc.these.typePh.Den_US
dc.titleA STUDY OF SPATIAL BOOTSTRAP TECHNIQUES FOR VARIANCE ESTIMATION IN FINITE POPULATIONen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Thesis_Full_Ankur Biswas 9643 PhD Ag Stat.pdf
Size:
4.87 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Theses