Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clustering algorithms are explored to partition the unlabeled instances into different strata with each stratum represented by a center. Then, diversity-motivated intra-stratum sampling is adopted to choose the center and addi...

In this paper, a log type calibration estimator for estimating the population mean in stratified random sampling has been proposed utilizing available auxiliary information. This also extended case of double when information is not known. The simulation study carried out using R software on real as well artificial datasets. empirical bootstrap estimates percentage relative root squared error (%...

Decision-making in survey sampling planning is a tricky situation; sometimes it involves multiple objectives, with various decision variables emanating from heterogeneous and homogeneous populations. Dealing the entire population under study its uncertain nature becomes challenging issue for researchers policymakers. Hence, an appropriate design optimization methodology are imperative. The pres...

Stratified random sampling at differential rates is commonly used in social science surveys to improve the chances of getting sufficiently large samples of subpopulations of interest. Multi-phase sampling is often used to further target desired populations. The variances of such samples can be estimated using jackknifing, which creates replicated values of the characteristic of interest as if o...