New weighting strategies for spatially correlated Poisson sampling

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.