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dc.contributor.advisorCoull, Brenten_US
dc.contributor.advisorDominici, Francescaen_US
dc.contributor.advisorZigler, Corwinen_US
dc.contributor.advisorSchwartz, Joelen_US
dc.contributor.authorAntonelli, Josephen_US
dc.date.accessioned2016-04-21T18:06:56Z
dash.embargo.terms2018-03-01en_US
dc.date.created2016-03en_US
dc.date.issued2015-12-04en_US
dc.date.submitted2016en_US
dc.identifier.citationAntonelli, Joseph. 2016. Statistical Methods for Analyzing Complex Spatial and Missing Data. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:26718722
dc.description.abstractIn chapter 1, we develop a novel two-dimensional wavelet decomposition to decompose spatial surfaces into different frequencies without imposing any restrictions on the form of the spatial surface. We illustrate the effectiveness of the proposed decomposition on satellite based PM2.5 data, which is available on a 1km by 1km grid across Massachusetts. We then apply our proposed decomposition to study how different frequencies of the PM2.5 surface adversely impact birth weights in Massachusetts. In chapter 2, we study the impact of monitor locations on two stage health effect studies in air pollution epidemiology. Typically in these studies, estimates of air pollution exposure are obtained from a first stage model that utilizes monitoring data, and then a second stage outcome model is fit using this estimated exposure. The location of the monitoring sites is usually not random and their locations can drastically impact inference in health effect studies. We take an in-depth look at the specific case where the location of monitors depends on the locations of the subjects in the second stage model and show that inference can be greatly improved in this setting relative to completely random allocation of monitors. In chapter 3, we introduce a Bayesian data augmentation method to control for confounding in large administrative databases when additional data is available on confounders in a validation study. Large administrative databases are becoming increasingly available, and they have the power to address many questions that we otherwise couldn't answer. Most of these databases, while large in size, do not have sufficient information on confounders to validly estimate causal effects. However, in many cases a smaller, validation data set is available with a richer set of confounders. We propose a method that uses information from the validation data to impute missing confounders in the main data and select only those confounders which are necessary for confounding adjustment. We illustrate the effectiveness of our method in a simulation study, and analyze the effect of surgical resection on 30 day survival in brain tumor patients from Medicare.en_US
dc.description.sponsorshipBiostatisticsen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dash.licenseLAAen_US
dc.subjectBiology, Biostatisticsen_US
dc.subjectStatisticsen_US
dc.titleStatistical Methods for Analyzing Complex Spatial and Missing Dataen_US
dc.typeThesis or Dissertationen_US
dash.depositing.authorAntonelli, Josephen_US
dc.date.available2018-03-01T08:31:07Z
thesis.degree.date2016en_US
thesis.degree.grantorGraduate School of Arts & Sciencesen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
dc.type.materialtexten_US
thesis.degree.departmentBiostatisticsen_US
dash.identifier.vireohttp://etds.lib.harvard.edu/gsas/admin/view/699en_US
dc.description.keywordsMultiresolution analysis; Spatial statistics; Environmental statistics; Causal inference; Missing dataen_US
dash.author.emailjantonelli111@gmail.comen_US
dash.identifier.drsurn-3:HUL.DRS.OBJECT:26752194en_US
dash.contributor.affiliatedAntonelli, Joseph


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