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On unequal probability sampling designsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2010 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Umeå: Department of Mathematics and Mathematical Statistics, Umeå University , 2010. , p. 31 + 6 papers
##### Keyword [en]

conditional Poisson sampling, correlated Poisson sampling, entropy, extended Sampford sampling, Horvitz-Thompson estimator, inclusion probabilities, list-sequential sampling, non-rejective implementation, Pareto sampling, Poisson sampling, probability functions, ratio estimator, real-time sampling, repeated Poisson sampling, Sampford sampling, sampling designs, splitting method, unequal probability sampling
##### National Category

Probability Theory and Statistics
##### Research subject

Mathematical Statistics
##### Identifiers

URN: urn:nbn:se:umu:diva-33701ISBN: 978-91-7264-999-6 (print)OAI: oai:DiVA.org:umu-33701DiVA, id: diva2:317506
##### Public defence

2010-05-28, MIT-huset, MA 121, Umeå universitet, Umeå, 13:15 (English)
##### Opponent

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##### Supervisors

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#####

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Available from: 2010-05-07 Created: 2010-05-03 Last updated: 2010-05-18Bibliographically approved
##### List of papers

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. The inclusion probabilities are usually chosen to be proportional to some auxiliary variable that is known for all units in the population. When unequal probability sampling is applicable, it generally gives much better estimates than sampling with equal probabilities. This thesis consists of six papers that treat unequal probability sampling from a finite population of units.

A random sample is selected according to some specified random mechanism called the sampling design. For unequal probability sampling there exist many different sampling designs. The choice of sampling design is important since it determines the properties of the estimator that is used. The main focus of this thesis is on evaluating and comparing different designs. Often it is preferable to select samples of a fixed size and hence the focus is on such designs.

It is also important that a design has a simple and efficient implementation in order to be used in practice by statisticians. Some effort has been made to improve the implementation of some designs. In Paper II, two new implementations are presented for the Sampford design.

In general a sampling design should also have a high level of randomization. A measure of the level of randomization is entropy. In Paper IV, eight designs are compared with respect to their entropy. A design called adjusted conditional Poisson has maximum entropy, but it is shown that several other designs are very close in terms of entropy.

A specific situation called real time sampling is treated in Paper III, where a new design called correlated Poisson sampling is evaluated. In real time sampling the units pass the sampler one by one. Since each unit only passes once, the sampler must directly decide for each unit whether or not it should be sampled. The correlated Poisson design is shown to have much better properties than traditional methods such as Poisson sampling and systematic sampling.

1. Repeated poisson sampling$(function(){PrimeFaces.cw("OverlayPanel","overlay202270",{id:"formSmash:j_idt480:0:j_idt484",widgetVar:"overlay202270",target:"formSmash:j_idt480:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Non-rejective implementations of the Sampford sampling design$(function(){PrimeFaces.cw("OverlayPanel","overlay202271",{id:"formSmash:j_idt480:1:j_idt484",widgetVar:"overlay202271",target:"formSmash:j_idt480:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. On a generalization of Poisson sampling$(function(){PrimeFaces.cw("OverlayPanel","overlay148060",{id:"formSmash:j_idt480:2:j_idt484",widgetVar:"overlay148060",target:"formSmash:j_idt480:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Entropy of unequal probability sampling designs$(function(){PrimeFaces.cw("OverlayPanel","overlay290870",{id:"formSmash:j_idt480:3:j_idt484",widgetVar:"overlay290870",target:"formSmash:j_idt480:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. An extension of Sampford's method for unequal probability sampling$(function(){PrimeFaces.cw("OverlayPanel","overlay317394",{id:"formSmash:j_idt480:4:j_idt484",widgetVar:"overlay317394",target:"formSmash:j_idt480:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

6. Efficient sampling when the inclusion probabilities do not sum to an integer$(function(){PrimeFaces.cw("OverlayPanel","overlay317393",{id:"formSmash:j_idt480:5:j_idt484",widgetVar:"overlay317393",target:"formSmash:j_idt480:5:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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