Sampling and Estimation in Hidden Population Using Social Network

Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

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Social network sampling using spanning trees

Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves t...

Estimating hidden population size using Respondent-Driven Sampling data.

Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female s...

Some Improvment in the Estimation of Population Mean in Cluster Sampling

Gupta and Shabbir (2008) have suggested an alternative form of ratio-type estimator for estimating the population mean. In this paper, we introduced new estimators by mixing two, stratified and cluster sampling method. Then we improved these estimators by using auxiliary variables and introducing new estimators. For sampling in infinite populations with a high geographic dispersion, the populat...

Story Segmentation Using Social Network Analysis and Hidden Markov Models