Search for dissertations about: "unequal probability sampling"
Showing result 1 - 5 of 7 swedish dissertations containing the words unequal probability sampling.
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1. On unequal probability sampling designs
Abstract : The main objective in sampling is to select a sample from a population in order to estimate some unknown population parameter, usually a total or a mean of some interesting variable. When the units in the population do not have the same probability of being included in a sample, it is called unequal probability sampling. READ MORE
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2. Contributions to the theory of unequal probability sampling
Abstract : This thesis consists of five papers related to the theory of unequal probability sampling from a finite population. Generally, it is assumed that we wish to make modelassisted inference, i.e. the inclusion probability for each unit in the population is prescribed before the sample is selected. READ MORE
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3. Unequal Probability Sampling in Active Learning and Traffic Safety
Abstract : This thesis addresses a problem arising in large and expensive experiments where incomplete data come in abundance but statistical analyses require collection of additional information, which is costly. Out of practical and economical considerations, it is necessary to restrict the analysis to a subset of the original database, which inevitably will cause a loss of valuable information; thus, choosing this subset in a manner that captures as much of the available information as possible is essential. READ MORE
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4. Optimal Subsampling Designs Under Measurement Constraints
Abstract : We consider the problem of optimal subsample selection in an experiment setting where observing, or utilising, the full dataset for statistical analysis is practically unfeasible. This may be due to, e.g., computational, economic, or even ethical cost-constraints. READ MORE
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5. Techniques to Calculate Exact Inclusion Probabilities for Conditional Poisson Sampling and Pareto .pi.ps Sampling Designs
Abstract : This thesis consists of five papers and treats two finite population sampling methods, viz. the Conditional Poisson and the Pareto .pi.ps sampling schemes. READ MORE