BibTeX
@MISC{_apts2012/13:,
author = {},
title = {APTS 2012/13: Spatial and Longitudinal Data Analysis Preliminary Material},
year = {}
}
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Abstract
Aims: This module will introduce students to the statistical concepts and tools involved in modelling data which are correlated in time and/or space. Learning outcomes: By the end of the module, students should have achieved: • a clear understanding of the meaning of temporal and spatial correlation; • a good working knowledge of standard models to describe both the systematic and the random parts of an appropriate model; • the ability to implement and interpret these models in standard applications; • an understanding of some of the key concepts which lie at the heart of current research in this area; • appreciation of at least one substantial case study. Prerequisites: Preparation for this module should establish familiarity with: • standard models and tools for time series data, at the level of a typical undergraduate course on time series; • standard models and tools for spatial data at its simplest level; • inferential methods, including classical and Bayesian likelihood-based methods, to at
Keyphrases
longitudinal data analysis preliminary material standard model time series data appropriate model inferential method substantial case study typical undergraduate course spatial data key concept time series current research clear understanding good working knowledge random part standard application bayesian likelihood-based method statistical concept spatial correlation
