Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Mathematics > Statistics Theory

arXiv:1803.04484 (math)
[Submitted on 12 Mar 2018]

Title:Adaptive two-stage sequential double sampling

Authors:Bardia Panahbehagh, Afshin Parvardeh, Babak Mohammadi
View PDF
Abstract:In many surveys inexpensive auxiliary variables are available that can help us to make more precise estimation about the main variable. Using auxiliary variable has been extended by regression estimators for rare and cluster populations. In conventional regression estimator it is assumed that the mean of auxiliary variable in the population is known. In many surveys we don't have such wide information about auxiliary variable. In this paper we present a multi-phase variant of two-stage sequential sampling based on an inexpensive auxiliary variable associated with the survey variable in the form of double sampling. The auxiliary variable will be used in both design and estimation stage. The population mean is estimated by a modified regression-type estimator with two different coefficient. Results will be investigated using some simulations following Median and Thompson (2004).
Subjects: Statistics Theory (math.ST)
Cite as: arXiv:1803.04484 [math.ST]
  (or arXiv:1803.04484v1 [math.ST] for this version)
  https://doi.org/10.48550/arXiv.1803.04484

Submission history

From: Bardia Panahbehagh Ph.D. [view email]
[v1] Mon, 12 Mar 2018 19:38:52 UTC (12 KB)
Full-text links:

Access Paper:

view license
Current browse context:
math.ST
< prev   |   next >
new | recent | 2018-03
Change to browse by:
math
stat
stat.TH

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)