By George A.F. Seber, Mohammad M. Salehi (auth.)
This e-book goals to supply an outline of a few adaptive innovations utilized in estimating parameters for finite populations the place the sampling at any level will depend on the sampling details bought to this point. The pattern adapts to new details because it is available in. those tools are specially used for sparse and clustered populations.
Written through stated specialists within the box of adaptive sampling.
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Additional resources for Adaptive Sampling Designs: Inference for Sparse and Clustered Populations
Su and Quinn II (2003) used order statistics but with a stopping rule that led to biased estimators. The HT estimator was preferred as it was less sensitive to the stopping level. One area of development relates to the problem of dealing with hidden human populations such as the Internet and other networked structures. These can be conceptualized mathematically as graphs and are hard to sample by conventional methods. The most effective sampling method is an adaptive one following links from one node to another, rather like a random walk.
Seber. 1997. ” Biometrics 53:959– 970. F. Seber. 2002. ” Australian and New Zealand Journal of Statistics 44:63–74. , B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag. K. 1991a. ” Biometrics 47:1103–1115. K. 1991b. ” Biometrika 78:389–397. F. Seber. 1996. Adaptive Sampling. New York: Wiley. Chapter 5 Inverse Sampling Methods Abstract Inverse sampling is an adaptive method whereby it is the sample size that is adaptive. On the basis of a new proof, Murthy’s estimator can now be applied with or without adaptive cluster sampling to inverse sampling to provide unbiased estimators of the mean and variance of the mean estimator.
The next initial unit is selected from what is left and we continue the process until we have selected n initial units thus giving n distinct networks without replacement. Let xk be the number of units in network k (k = 1, 2, . . n), and define yk∗ = xk ∗ j=1 y j , the y-value of the network, and wk = yk /x k , the mean of the y-values N yi = kk=1 yk∗ and μ = τ/N . We can use Murthy’s in network k. Then τ = i=1 estimator from Eq. 3) with the basic sampling unit now being the network (with yi replaced by yi∗ ).
Adaptive Sampling Designs: Inference for Sparse and Clustered Populations by George A.F. Seber, Mohammad M. Salehi (auth.)