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Abstract:
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Our world is uncertain, our attempts to explain the world (science) are uncertain, and our measurements of our (uncertain) world are uncertain. Science's passion is to answer the "Why" question and, in its pursuit, it often comes across the "Where" and "When" questions. Good data collection involves a protocol that says where (i.e., spatial locations) and when (i.e., temporal instants) the measurements were taken. Hence, it is possible to think about uncertainties as being partly explained by spatial variability and temporal variability. Conversely, if those variabilities are ignored, the "Why" question might be posed and answered incorrectly. Using environmental data sets, I shall explore the statistical methodologies that incorporate spatio-temporal variability, with particular emphasis on hierarchical statistical models.
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