By Timothy G. Gregoire, Harry T. Valentine
PREFACEINTRODUCTIONThe desire for sampling recommendations A medley of sampling scenariosProbability sampleInference inhabitants descriptive parametersHistorical word phrases to recollect AppendixSAMPLING DISTRIBUTION OF AN ESTIMATOR Distribution of values Estimation period estimation The function of simulated sampling different issues phrases to recollect ExercisesAppendixSAMPLING DESIGNS FOR DISCRETE POPULATIONS Introduction equivalent likelihood designsUnequal chance designsTerms to recollect routines AppendixSAMPLING DESIGNS FOR non-stop POPULATIONS creation Crude Monte CarloImportance sampling. Read more...
summary: PREFACEINTRODUCTIONThe desire for sampling suggestions A medley of sampling scenariosProbability sampleInference inhabitants descriptive parametersHistorical word phrases to recollect AppendixSAMPLING DISTRIBUTION OF AN ESTIMATOR Distribution of values Estimation period estimation The position of simulated sampling different issues phrases to recollect ExercisesAppendixSAMPLING DESIGNS FOR DISCRETE POPULATIONS creation equivalent chance designsUnequal chance designsTerms to recollect routines AppendixSAMPLING DESIGNS FOR non-stop POPULATIONS creation Crude Monte CarloImportance sampling
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In this section we present three such designs: simple random sampling, systematic sampling, and Bernoulli sampling. Simple random sampling may be applied with or without the replacement of population elements. Both systematic sampling and Bernoulli sampling are applied without the replacement of population elements. 1 Simple random sampling The simple random sampling design is one in which all possible samples of fixed size, n, are equally likely. Conversely, any design which ensures that all possible samples of n elements are equally likely is a simple random sampling design.
In an analogous fashion, an estimator is an algebraic expression that one evaluates with the data from the sample in order to provide a quantitative estimate of the target parameter. For the moment, let represent the population parameter of interest, and let O represent an estimator of it. We presume that the value of is unknown and that it is infeasible to measure all N elements of the population in order to evaluate it. Hence the need to select and measure n < N units in order to estimate .
1 Introduction Designs for selecting a sample from populations comprising discrete elements are presented in this chapter. For each design, one or more estimators of the population total, y , or mean value per element, y , are also presented. Integral to this discussion is the consideration of the probability with which samples are selected and the probability with which individual elements of the population are included into the sample. 2 Equal probability designs Equal-probability designs impose the same inclusion probability on each element of a population.