NDLI: Variances and variance estimators of the improved ratio estimators under adaptive cluster sampling

Content Provider	Springer Nature Link
Author	Lin, Feng Min Chao, Chang Tai
Copyright Year	2013
Abstract	Although not design-unbiased, the ratio estimator is recognized as more efficient when a certain degree of correlation exists between the variable of primary interest and the auxiliary variable. Meanwhile, the Rao–Blackwell method is another commonly used procedure to improve estimation efficiency. Various improved ratio estimators under adaptive cluster sampling (ACS) that make use of the auxiliary information together with the Rao–Blackwellized univariate estimators have been proposed in past research studies. In this article, the variances and the associated variance estimators of these improved ratio estimators are proposed for a thorough framework of statistical inference under ACS. Performance of the proposed variance estimators is evaluated in terms of the absolute relative percentage bias and the empirical mean-squared error. As expected, results show that both the absolute relative percentage bias and the empirical mean-squared error decrease as the initial sample size increases for all the variance estimators. To evaluate the confidence intervals based on these variance estimators and the finite-population Central Limit Theorem, the coverage rate and the interval width are used. These confidence intervals suffer a disadvantage similar to that of the conventional ratio estimator. Hence, alternative confidence intervals based on a certain type of adjusted variance estimators are constructed and assessed in this article.
Starting Page	285
Ending Page	311
Page Count	27
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	21
Issue Number	2
e-ISSN	15733009
Language	English
Publisher	Springer US
Publisher Date	2013-06-08
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adaptive cluster sampling Confidence interval Minimal sufficient statistic Ratio estimator Sufficient statistic Variance estimator Ecology Statistics Mathematical and Computational Biology Evolutionary Biology
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Leveraging the Rao–Blackwell theorem to improve ratio estimators in adaptive cluster sampling

Randomized graph sampling

Bootstrap confidence intervals for adaptive cluster sampling design based on Horvitz–Thompson type estimators

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling with clusters selected without replacement and stopping rule

Performance of adaptive cluster sampling estimators in a multivariate setting

A review of adaptive cluster sampling: 1990–2003

Comparison between Hansen-Hurwitz and Horvitz-Thompson estimators for adaptive cluster sampling

Design based estimation for ranked set sampling in finite populations

Variances and variance estimators of the improved ratio estimators under adaptive cluster sampling

Similar Documents

Leveraging the Rao–Blackwell theorem to improve ratio estimators in adaptive cluster sampling

Randomized graph sampling

Bootstrap confidence intervals for adaptive cluster sampling design based on Horvitz–Thompson type estimators

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling with clusters selected without replacement and stopping rule

Performance of adaptive cluster sampling estimators in a multivariate setting

A review of adaptive cluster sampling: 1990–2003

Comparison between Hansen-Hurwitz and Horvitz-Thompson estimators for adaptive cluster sampling

Design based estimation for ranked set sampling in finite populations

Variances and variance estimators of the improved ratio estimators under adaptive cluster sampling