文献类型：期刊文献

英文题名：Regression-type estimators for adaptive two-stage sequential sampling

作者：Salehi, Mohammad[1,2] Panahbehagh, Bardia[2] Parvardeh, Afshin[3] Smith, David R.[4] Lei, Yuancai[5]

第一作者：Salehi, Mohammad

通信作者：Salehi, M[1]

机构：[1]Qatar Univ, Dept Math Stat & Phys, Doha, Qatar;[2]Isfahan Univ Technol, Dept Math Sci, Esfahan, Iran;[3]Univ Isfahan, Dept Stat, Esfahan, Iran;[4]US Geol Survey, Leetown Sci Ctr, Kearneysville, WV USA;[5]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China

年份：2013

卷号：20

期号：4

起止页码：571-590

外文期刊名：ENVIRONMENTAL AND ECOLOGICAL STATISTICS

收录：;Scopus(收录号：2-s2.0-84888061883);WOS:【SCI-EXPANDED(收录号:WOS:000327086000004)】；

基金：We would like to thank two referees and an associate editor for their helpful suggestions on a previous draft of this article. The authors express their appreciation to the Ministry of Science and Technology and National Natural Sciences Foundation of China for fiscal support in the field work of Tamarix ramosissima population (Project Research Grants 31170588 and 2005DIB5JI42).

语种：英文

外文关键词：Adaptive sampling; Freshwater mussels; GIS auxiliary variable; Optimal coefficient; Rare population; Tamarix ramosissima

摘要：Adaptive two-stage sequential sampling (ATSSS) design was developed to observe more rare units and gain higher efficiency, in the sense of having a smaller variance estimator, than conventional sampling designs with equal effort for rare and spatially cluster populations. For certain rare populations, incorporating auxiliary variables into a sampling design can further improve the observation of rare units and increase efficiency. In this article, we develop regression-type estimators for ATSSS so that auxiliary variables can be incorporated into the ATSSS design when warranted. Simulation studies on two populations show that the regression-type estimators can significantly increase the efficiency of ATSSS and the detection of more rare units as compared to conventional sampling counterparts. Simulation of sampling of desert shrubs in Inner Mongolia (one of the two populations studied) showed that by incorporating a GIS auxiliary variable into ATSSS with the regression estimators resulted in a gain in efficiency over ATSSS without the auxiliary variable. Further, we found that the use of the GIS auxiliary variable in a conventional two-stage design with a regression estimator did not show a gain in efficiency.