A theoretical overview for variance estimation in sampling theory with some new techniques for complex estimators

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dc.contributor University of Helsinki, Faculty of Social Sciences, Department of Mathematics and Statistics en
dc.contributor Helsingin yliopisto, Valtiotieteellinen tiedekunta, Matematiikan ja tilastotieteen laitos fi
dc.contributor Helsingfors universitet, Statsvetenskapliga fakulteten, Matematiska och statistiska institutionen sv
dc.contributor.author Ollila, Pauli
dc.date.accessioned 2009-09-08T10:12:19Z
dc.date.available 2009-09-08T10:12:19Z
dc.date.issued 2004-09-03
dc.identifier.uri http://hdl.handle.net/10138/13523
dc.description Endast sammandrag. Inbundna avhandlingar kan sökas i Helka-databasen (http://www.helsinki.fi/helka). Elektroniska kopior av avhandlingar finns antingen öppet på nätet eller endast tillgängliga i bibliotekets avhandlingsterminaler. sv
dc.description Only abstract. Paper copies of master’s theses are listed in the Helka database (http://www.helsinki.fi/helka). Electronic copies of master’s theses are either available as open access or only on thesis terminals in the Helsinki University Library. en
dc.description Vain tiivistelmä. Sidottujen gradujen saatavuuden voit tarkistaa Helka-tietokannasta (http://www.helsinki.fi/helka). Digitaaliset gradut voivat olla luettavissa avoimesti verkossa tai rajoitetusti kirjaston opinnäytekioskeilla. fi
dc.description.abstract When making judgements of the quality of survey sampling, especially with the complex estimators, the method of variance estimation is of importance. In addition to the well-known linearisation approach, there are many methods based on the sample reuse. The variety of these methods, especially in sampling theory, does not have any unified theoretical framework. The first part of the thesis is a theoretical overview for variance estimation, covering the foundations of the sampling theory and the current methodology of variance estimation. Also new methods and theoretical results are provided in the thesis. The cumulants and k-statistics are utilised to study the theoretical correction coefficient of unbiased variance estimation. Some examples of this approach are given for the variance estimation of the estimator of the population variance. The post-design vectors, i.e. artificially expanded design vectors for variance estimation, are used as the scale adjustment needed to correct the effect of the difference between the sampling design and resampling design. There are also correction methods utilising two-phase resample spaces and alternatively two resample to introduce various estimator-dependent scale corrections. New sampling distribution results concerning without-replacement and with-replacement designs in two-phase sampling situations are presented. A variance decomposition approach utilising sample pair probabilities is given with variance estimators. Finally, both old and new methods are tested with two real small populations. Results reveal e.g. that the two-phase resample approach deminishes the bias almost for every estimator studied, and for one estimator the sample pair probability approach provides unbiased variance estimator. en
dc.language.iso en
dc.subject sampling theory en
dc.subject variance estimation en
dc.subject complex estimators en
dc.subject resampling methods en
dc.title A theoretical overview for variance estimation in sampling theory with some new techniques for complex estimators en
dc.identifier.laitoskoodi 710
dc.description.note Research Reports of Statistics Finland, ISSN 0355-2071 en
dc.type.ontasot Doctoral thesis en
dc.type.ontasot Väitöskirja fi
dc.type.ontasot Doktorsavhandling sv
dc.type.dcmitype Text
dc.format.content abstractOnly

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