Hubbard, Ben Arthur (2014) Parameter Redundancy with Applications in Statistical Ecology. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:47436)
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Abstract
This thesis is concerned with parameter redundancy in statistical ecology models. If it is not possible to estimate all the parameters, a model is termed parameter redundant. Parameter redundancy commonly occurs when parameters are confounded in the model so that the model could be reparameterised in terms of a smaller number of parameters. In principle, it is possible to use symbolic algebra to determine whether or not all the parameters of a certain ecological model can be estimated using classical methods of statistical inference.
We examine a variety of different ecological models: We begin by exploring models based on marking a number of animals and observing the same animals at future time points. These observations can either be when the animal is marked and then
estimates of the probability of presence, or absence, for living species by the use of repeated detection surveys, where these models have the advantage that individuals are not required to be marked. A variety of different occupancy models are examined included the addition of seasondependent parameters, groupdependent parameters and speciesdependent, along with other models.
We investigate parameter redundancy by deriving general results for a variety of different models where the model's parameter dependencies can be relaxed suited to different studies. We also analyse how the results change for specific data sets and how sparse data influence whether or not a model is parameter redundant using procedures written in Maple. This theory on parameter redundancy is vital for the correct use of these ecological models so that valid statistical inference can be made.
Item Type:  Thesis (Doctor of Philosophy (PhD)) 

Thesis advisor:  Cole, Diana 
Thesis advisor:  Morgan, Byron 
Uncontrolled keywords:  Parameter Redundancy, Ecology, Identifiability, Markrecovery, Capturerecapture, Capturerecapturerecovery, Occupancy modelling, Exhaustive summary, Derivative matrix 
Subjects:  Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics 
Divisions:  Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science 
Depositing User:  Users 1 not found. 
Date Deposited:  27 Feb 2015 12:12 UTC 
Last Modified:  16 Feb 2021 13:23 UTC 
Resource URI:  https://kar.kent.ac.uk/id/eprint/47436 (The current URI for this page, for reference purposes) 
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