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Titlebook: Sampling Theory and Practice; Changbao Wu,Mary E. Thompson Book 2020 Springer Nature Switzerland AG 2020 Basic Sampling Theory.Bayesian In

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Dual Frame and Multiple Frame Surveysod methods and the concept of multiplicity are shown to be very useful for point estimation and confidence intervals, especially for estimating population means of binary study variables such as the prevalence of a disease or the proportion of the population with a rare condition.
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Book 2020rent research trends, and informed discussion of the problems commonly encountered in survey practice. These related aspects of survey methodology rarely appear together under a single connected roof, making this book a unique combination of materials for teaching, research and practice in survey sa
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Simple Single-Stage Sampling Methodse for the probability selection of units for the survey sample. The . are the same as the individual population units. The term . distinguishes this kind of sampling from . or . sampling methods to be discussed in Chap. ., where the first stage sampling units are clusters.
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Stratified Sampling and Cluster Samplingts. In this chapter we provide some basic results on stratified sampling and cluster sampling. In Sect. 3.5 we provide a brief discussion on stratified two-stage cluster sampling, which reveals the notational complexities for complex surveys. General unequal probability sampling methods will be disc
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Model-Based Prediction and Model-Assisted Estimationiary population information. The two approaches, model-based prediction and design-based estimation, are conceptually different and have been the subject of debate among survey statisticians. The general model-assisted framework, however, enables the strength of plausible models to be built into des
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Calibration Weighting and Estimation may be available from census enumerations or administrative records. It could also be obtained from unusual sources such as satellite images for natural resource inventory surveys or a previous survey from the same population. Auxiliary information can be utilized at the survey design stage to faci
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Regression Analysis and Estimating Equationsach which combines model-based inferences and design-based estimation under a two-stage joint randomization framework. The formulation of problems starts with a general statistical model but the estimation procedures are mostly design-based. Both the model structure and the survey design features ar
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