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General covariance, artificial gauge freedom and empirical equivalence :Pitts, James Brian. January 2008 (has links)
Thesis (Ph. D.)--University of Notre Dame, 2008. / Thesis directed by Don Howard for the Department of History and Philosophy of Science. "July 2008." Includes bibliographical references (leaves 196-233).
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82 |
Nonparametric analysis of covariance based on residuals /Jackson, J. Michael, January 1997 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 431-432). Also available on the Internet.
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Nonparametric analysis of covariance based on residualsJackson, J. Michael, January 1997 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 431-432). Also available on the Internet.
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84 |
Three essays on modeling conditional correlation /Sheppard, Kevin, January 2004 (has links)
Thesis (Ph. D.)--University of California, San Diego, 2004. / Vita. Includes bibliographical references.
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85 |
Performance of AIC-selected spatial covariance structures for fMRI data /Stromberg, David A., January 2005 (has links) (PDF)
Thesis (M.S.)--Brigham Young University. Dept. of Statistics, 2005. / Includes bibliographical references (p. 63-64).
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86 |
Judgement post-stratification for designed experimentsDu, Juan, January 2006 (has links)
Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 143-146).
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87 |
Design and analysis of experiments in the presence of spatial correlationHooks, Tisha L. January 1900 (has links)
Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (sites viewed on August 11, 2006). PDF text of dissertation: 76 p. : ill. ; 1.32Mb. UMI publication number: AAT 3208130. Includes bibliographical references. Also available in microfilm, microfiche and paper format.
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Data-driven approach for control performance monitoring and fault diagnosisYu, Jie, January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.
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89 |
A continuous-time Markov chain approach for trinomial-outcome longitudinal data : an extension for multiple covariates.Mhoon, Kendra Brown. Moyé, Lemuel A., Mullen, Patricia D., Vernon, Sally W., January 2008 (has links)
Thesis (Ph. D.)--University of Texas Health Science Center at Houston, School of Public Health, 2008. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1086. Adviser: Wenyaw Chan. Includes bibliographical references.
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Using eddy covariance, remote sensing, and in situ observations to improve models of springtime phenology in temperate deciduous forestsMelaas, Eli Kellen 12 March 2016 (has links)
Phenological events in temperate forests, such as bud burst and senescence, exert strong control over seasonal fluxes of water, energy and carbon. The timing of these transitions is influenced primarily by air temperature and photoperiod, although the exact nature and magnitude of these controls is poorly understood. In this dissertation, I use in situ and remotely sensed observations of phenology in combination with surface meteorological data and measurements of biosphere-atmosphere carbon exchanges to improve understanding and develop models of canopy phenology in temperate forest ecosystems. In the first element of this research I use surface air temperatures and eddy covariance measurements of carbon dioxide fluxes to evaluate and refine widely used approaches for predicting the onset of photosynthesis in spring that account for geographic variation in thermal and photoperiod constraints on phenology. Results from this analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than existing models. A key challenge in developing and testing these models, however, is lack of adequate data sets that characterize phenology over large areas at multi-decadal time scales. To address this need, I develop a new method for estimating long-term average and interannual dynamics in the phenology of temperate forests using time series of Landsat TM/ETM+ images. Results show that estimated spring and autumn transition dates agree closely with in-situ measurements and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. In the final element of this dissertation, I use meteorological data, species composition maps, satellite remote sensing, and ground observations to develop models of springtime leaf onset in temperate deciduous forests that account for geographic differences in how forest communities respond to springtime climate forcing. Results demonstrate important differences in cumulative heating requirements and photoperiod cues among forest types and that regional differences in species composition explain substantial geographic variation in springtime phenology of temperate forests. Together, the results from this dissertation provide an improved basis for observing and modeling springtime phenology in temperate forests.
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