Estimation under purposive sampling - Guarte, Jacqueline Mendoza 1962 (2004)
Abstract (Purposive sampling is described as a random selection of sampling units within the segment of the population with the most information on the characteristic of interest. The procedure effectively assigns zero probability of inclusion to units towards the tails of the distribution believed to have minimal information or none at all. This sampling strategy is shown to be optimal under the Bayesian framework. Simulation of the posterior risk indicate lower risk levels of the sample mean as estimator under purposive sampling relative to those under simple random sampling. This was consistently shown under the squared error loss function as effects of parameters of the posterior risk on its level were simulated. Nonparametric bootstrap is proposed in estimating location parameters and the corresponding variances under purposive sampling. An estimate of bias and a measure of variance of the point estimate are computed using the Monte Carlo method. The bootstrap estimator of the population mean is efficient and consistent in the homogenous, heterogenous and two-segment populations simulated. The design-unbiased approximation of the standard error estimate differs substantially from the bootstrap estimate in severely heterogenous and positively skewed populations. Effects of number of replications and resample size on bootstrap estimates are also discussed).
"October 2004."
Vita.
Thesis (M.S. Statistics)--University of the Philippines, Diliman.
Keywords: Sampling ( Statistics ).; Estimation theory