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Estimation Under Purposive Sampling
Authors:Jacqueline M Guarte
Institution:Department of Mathematics, Physics, and Statistics , Leyte State University , Visca , Baybay , Philippines
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. Nonparametric bootstrap is proposed in estimating location parameters and the corresponding variances. 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 homogeneous, heterogeneous, and two-segment populations simulated. The design-unbiased approximation of the standard error estimate differs substantially from the bootstrap estimate in severely heterogeneous and positively skewed populations.
Keywords:Design-unbiased approximation  Heterogeneous  and two-segment populations  Homogeneous  Nonparametric bootstrap
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