Two-Stage Inverse Adaptive Cluster Sampling

2021 ◽  
pp. 193-202
Author(s):  
Raosaheb Latpate ◽  
Jayant Kshirsagar ◽  
Vinod Kumar Gupta ◽  
Girish Chandra
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

Two-Stage Negative Adaptive Cluster Sampling

2021 ◽  
pp. 241-255
Author(s):  
Raosaheb Latpate ◽  
Jayant Kshirsagar ◽  
Vinod Kumar Gupta ◽  
Girish Chandra
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

Adaptive Cluster Sampling in Two-stage Sampling

2014 ◽  
Vol 56 (4) ◽  
pp. 347-357
Author(s):  
Mohammad Moradi ◽  
Jennifer A. Brown ◽  
Weilong Guo
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

Two-Stage Adaptive Cluster Sampling

Biometrics ◽  
1997 ◽  
Vol 53 (3) ◽  
pp. 959 ◽  
Author(s):  
Mohammad Salehi M. ◽  
George A. F. Seber
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

Two Stage Inverse Adaptive Cluster Sampling With Stopping Rule Depends upon the Size of Cluster

Sankhya B ◽  
2018 ◽  
Vol 82 (1) ◽  
pp. 70-83 ◽  
Author(s):  
Raosaheb V. Latpate ◽  
Jayant K. Kshirsagar
Keyword(s):  
Stopping Rule ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

Two-stage adaptive cluster sampling

2005 ◽  
Vol 14 (1) ◽  
pp. 3-10
Author(s):  
Stefania Naddeo ◽  
Caterina Pisani
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

scholarly journals Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255256
Author(s):  
Mohammad Salehi ◽  
David R. Smith
Keyword(s):  
Binary Data ◽  
Sampling Design ◽  
A Priori ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Inverse Sampling ◽  
Two Stage ◽  
Population Total ◽  
Unbiased Estimators ◽  
Final Sample

Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.

Two-Stage Negative Adaptive Cluster Sampling

2018 ◽  
Vol 8 (1) ◽  
pp. 1-21 ◽  
Author(s):  
R. V. Latpate ◽  
J. K. Kshirsagar
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

Adjusted two-stage adaptive cluster sampling

2002 ◽  
Vol 126 (1) ◽  
pp. 83-95
Author(s):  
A. Khan ◽  
H.A. Muttlak
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage

scholarly journals Two stage adaptive cluster sampling based on ordered statistics

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Girish Chandra ◽  
Neeraj Tiwari ◽  
Raman Nautiyal
Keyword(s):  
Random Sampling ◽  
Real Life ◽  
Simple Random Sampling ◽  
The Other ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage ◽  
Life Data ◽  
Real Life Data ◽  
Ordered Statistics

The estimation problem on sparsely distributed populations using adaptive cluster sampling (ACS) is discussed. In the first phase of ACS, two stage sampling is used in which primary and secondary sampling units are selected using simple random sampling without replacement. The idea of Thompson (1996) is introduced in order to choose an appropriate fixed value of pre-specified condition, which might represent the number of rare species, before conducting the survey by the use of order statistics. Different estimators of the population mean under the two possible schemes (open and closed boundaries of primary sampling units) are studied and the Rao-Blackwell theorem for improving these estimators is also used. Numerical illustrations, one on real life data and the other based on simulation study, are discussed for these two schemes. This design may be quite useful in environmental, forestry and other areas of research dealing with rare, endangered or threatened species.

Two-Stage Adaptive Cluster Sampling

2021 ◽  
pp. 157-169
Author(s):  
Raosaheb Latpate ◽  
Jayant Kshirsagar ◽  
Vinod Kumar Gupta ◽  
Girish Chandra
Keyword(s):  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Two Stage
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