Adaptive Sampling Designs: Inference for Sparse and Clustered PopulationsThis book aims to provide an overview of some adaptive techniques used in estimating parameters for finite populations where the sampling at any stage depends on the sampling information obtained to date. The sample adapts to new information as it comes in. These methods are especially used for sparse and clustered populations. |
Contents
1 Basic Ideas | 1 |
2 Adaptive Cluster Sampling | 11 |
3 RaoBlackwell Modifications | 27 |
4 Primary and Secondary Units | 37 |
5 Inverse Sampling Methods | 49 |
6 Adaptive Allocation | 61 |
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Common terms and phrases
adaptive allocation Adaptive Cluster Sampling Adaptive Sampling Designs biased Biometrics Biometrika Borkowski confidence intervals consider conventional denote distinct networks Dryver Ecological Statistics edge units efficient Environmental and Ecological example Félix-Medina final sample first-phase sample G.A.F. Seber HH estimator HT and HH hypergeometric distribution indicator variable Inverse Sampling Design Journal of Statistical kth network Latin square likelihood function mators Multiple Inverse Sampling Murthy’s estimator networks selected number of distinct number of units observations order statistics P(sR phase pling population mean primary unit PSUs quota sampling Rao-Blackwell theorem Restricted Adaptive Cluster S.K. Thompson Salehi and Seber sample mean sample of primary sample unit sampling without replacement secondary units Sect selected without replacement simple random sample strata stratified sampling stratum h sufficient statistic takes the value theory Thompson and Seber unbiased estimator unbiased variance estimate unequal probability sampling unit selected units in stratum unordered y-values