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Path sampling

by Mena Patummasut

Title:

Path sampling

Author(s):

Mena Patummasut

Advisor:

Dryver, Arthur L, advisor

Degree name:

Doctor of Philosophy

Degree level:

Doctoral

Degree discipline:

Statistics

Degree department:

School of Applied Statistics

Degree grantor:

National Institute of Development Administration

Issued date:

2011

Digital Object Identifier (DOI):

10.14457/NIDA.the.2011.13

Publisher:

National Institute of Development Administration

Abstract:

In sampling spatial populations, one part of the cost is due to the distance travelled to observe all of the units in a sample. Cluster sampling is one such sampling design which is often used specifically to address this issue. Even in cluster sampling, the researcher may have to travel great distances from the cluster to cluster selected. In an optimal setting, when sampling costs are mainly a function of distance travelled, researchers could sample all of the units in the path travelled during the sampling. For this reason, the authors are introducing a new sampling design, called “path sampling,” which offers exactly the latter ability to sample all of the units in the researcher’s path traversed during the sampling. Path sampling is a design in which the researcher selects a path or paths from start to finish, as opposed to selecting units. By applying the Horvitz-Thompson estimator, path sampling offers unbiased estimators for both mean and variance. This dissertation covers the pros and cons of path sampling in comparison to simple random sampling, cluster sampling, and random walk sampling. The simulation results show that path sampling gives the smallest value of the expected number of units traveled for the same sample size among four sampling designs. Thus, path sampling has less traveling or less cost. However, path sampling is less efficient than cluster sampling, simple random sampling without replacement, and random walk sampling in the population with low variation of y-values among clusters. On the other hand, path sampling is more efficient than random walk sampling in a population with high variation of y-values among clusters. Moreover, path sampling is more efficient than cluster sampling and SRSWOR in a population with high variation of y-values among clusters with the path starting or ending point on high y-values.

Description:

Thesis (Ph.D. (Statistics))--National Institute of Development Administration, 2011

Subject(s):

Sampling (Statistics)

Resource type:

Dissertation

Extent:

x, 129 leaves : ill. ; 30 cm.

Type:

Text

File type:

application/pdf

Language:

eng

Rights:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

URI:

http://repository.nida.ac.th/handle/662723737/397
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ทรัพยากรสารสนเทศทั้งหมดในคลังปัญญา ใช้เพื่อประโยชน์ทางการเรียนการสอนและการค้นคว้าเท่านั้น และต้องมีการอ้างอิงแหล่งที่มาทุกครั้งที่นำไปใช้ ห้ามดัดแปลงเนื้อหา และทำสำเนาต่อ รวมถึงไม่ให้อนุญาตนำไปใช้ประโยชน์เพื่อการค้า ไม่ว่ากรณีใด ๆ ทั้งสิ้น



This item appears in the following Collection(s)

  • GSAS: Dissertations [166]

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Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.

Copyright © National Institute of Development Administration | สถาบันบัณฑิตพัฒนบริหารศาสตร์
Library and Information Center | สำนักบรรณสารการพัฒนา
Email: NIDAWR@nida.ac.th    Chat: Facebook Messenger    Facebook: NIDAWisdomRepository
 

 

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