Covers both classical and modern sampling design and estimation methods, focusing on methods for populations that are difficult to sample--elusive, rare, clustered or hard to detect. Divided into six parts, it covers basic sampling from simple random to unequal probability sampling; treats the use of auxiliary data with ratio and regression estimation and looks at the ideas of sufficient data and of model and design in practical sampling; discusses major useful designs, including stratified, cluster, systematic, multi-stage, double and network sampling; examines detectability methods for elusive populations; explains spatial sampling; and introduces adaptive sampling designs. Each chapter contains exercises with answers provided in the appendix.

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