TY - JOUR
ID - 17939
TI - New weighting strategies for spatially correlated Poisson sampling
JO - Journal of Advanced Mathematical Modeling
JA - JAMM
LA - en
SN - 2251-8088
AU - Farokhinia, Hadi
AU - Chinipardaz, Rahim
AU - Parham, Gholamali
AD - Department of Statistics, Faculty of Mathematics and Computer Science, Shahid Chamran University of
Ahvaz, Ahvaz, Iran.
Y1 - 2022
PY - 2022
VL - 12
IS - 4
SP - 494
EP - 505
KW - Correlated Poisson sampling
KW - Horvitz-Thompson estimator
KW - Poisson sampling
KW - Spatial sampling
DO - 10.22055/jamm.2022.41081.2049
N2 - In Poisson sampling, each unit is selected independently of the other units with a certain inclusion probability, and the sample size n(s) is a random variable with large variation. Also, if there exists a trend between units of the population, it causes bias in the estimated population parameter. So Poisson-correlated sampling (CPS) and Poisson-correlated spatial sampling (SCPS) were introduced as alternative methods to Poisson sampling that reduces changes in the sample size and bias in the estimated population parameter by weighting strategies. In this paper, new strategies for choosing weights are introduced and it is shown by simulation that the new weighting strategies increase the efficiency of the estimated parameter compared to the earlier weighting strategies.
UR - https://jamm.scu.ac.ir/article_17939.html
L1 - https://jamm.scu.ac.ir/article_17939_cab316be00a5e9c4b096bce8f5e175bf.pdf
ER -