Statistical inference with randomized nomination sampling

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dc.contributor.authorNourmohammadi, Mohammad
dc.contributor.examiningcommitteeDavies, Katherine (Statistics) Torabi, Mahmoud (Community Health Sciences) Thompson, Mary (University of Waterloo)en_US
dc.contributor.supervisorJafari Jozani, Mohammad (Statistics) Johnson, Brad C. (Statistics)en_US
dc.date.accessioned2015-01-06T21:50:09Z
dc.date.available2015-01-06T21:50:09Z
dc.date.issued2014-05en_US
dc.date.issued2014-08en_US
dc.degree.disciplineStatisticsen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractIn this dissertation, we develop several new inference procedures that are based on randomized nomination sampling (RNS). The first problem we consider is that of constructing distribution-free confidence intervals for quantiles for finite populations. The required algorithms for computing coverage probabilities of the proposed confidence intervals are presented. The second problem we address is that of constructing nonparametric confidence intervals for infinite populations. We describe the procedures for constructing confidence intervals and compare the constructed confidence intervals in the RNS setting, both in perfect and imperfect ranking scenario, with their simple random sampling (SRS) counterparts. Recommendations for choosing the design parameters are made to achieve shorter confidence intervals than their SRS counterparts. The third problem we investigate is the construction of tolerance intervals using the RNS technique. We describe the procedures of constructing one- and two-sided RNS tolerance intervals and investigate the sample sizes required to achieve tolerance intervals which contain the determined proportions of the underlying population. We also investigate the efficiency of RNS-based tolerance intervals compared with their corresponding intervals based on SRS. A new method for estimating ranking error probabilities is proposed. The final problem we consider is that of parametric inference based on RNS. We introduce different data types associated with different situation that one might encounter using the RNS design and provide the maximum likelihood (ML) and the method of moments (MM) estimators of the parameters in two classes of distributions; proportional hazard rate (PHR) and proportional reverse hazard rate (PRHR) models.en_US
dc.description.noteFebruary 2015en_US
dc.identifier.citationNourmohammadi, M., Jafari Jozani, M., & Johnson, B. C. (2014). Confidence intervals for quantiles in finite populations with randomized nomination sampling. Computational Statistics & Data Analysis, 73, 112-128.en_US
dc.identifier.citationNourmohammadi, M., Jozani, M. J., & Johnson, B. C. Nonparametric Confidence Intervals for Quantiles with Randomized Nomination Sampling. Sankhya A, 1-25.en_US
dc.identifier.urihttp://hdl.handle.net/1993/30150
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.publisherIndian Statistical Instituteen_US
dc.rightsopen accessen_US
dc.subjectRandomized nomination samplingen_US
dc.subjectConfidence intervalen_US
dc.subjectTolerance intervalen_US
dc.subjectFinite populationen_US
dc.subjectInfinite populationen_US
dc.subjectProportional hazard rate modelen_US
dc.subjectMaximum likelihooden_US
dc.subjectMethod of momenten_US
dc.titleStatistical inference with randomized nomination samplingen_US
dc.typedoctoral thesisen_US
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