Use this URL to cite or link to this record in EThOS:
Title: Statistical analysis of replicated spatial point patterns.
Author: Wilson, Helen Elizabeth.
ISNI:       0000 0001 3570 214X
Awarding Body: University of Lancaster
Current Institution: Lancaster University
Date of Award: 1998
Availability of Full Text:
Access from EThOS:
The field of pathology provides us with many opportunities for collecting replicated spatial data. Using an ordinary microscope, for example, we can digitise cell positions within windows imposed on pieces of tissue. Suppose now that we have some such replicated spatial data from several groups of individuals, where each point in each window represents a cell position. We seek to determine whether the spatial arrangement of cells differs between the groups. We propose and develop a new method which allows us to answer such questions, and apply it to some spatial neuro-anatomical data. We introduce point process theory, and extend the existing second order methods to deal with replicated spatial data. We conclude the first part of the thesis by defining Sudden Infant Death Syndrome (S.LD.S.) and Intra-Uterine Growth Retardation (LU.G.R.), and stating why these conditions are neuro-anato,mically interesting. We develop and validate a method for comparing groups of spatial data, which is motivated by analysis of variance, and uses a Monte Carlo procedure to attach significance to between-group differences. Having carried out our initial investigative work looking exclusively at the one-way set up, we extend the new methods to cope with two and higher way set ups, and again carry out some validation. We turn our attention to practical issues which arise in the collection of spatial neuroanatomical data. How, for example, should we collect the data to ensure the unbiasedness of any inference we may draw from it? We introduce the field of stereology which facilitates the unbiased sampling of tissue. We note a recent proposal to assess spatial distribution of cells using a stereological approach, and compare it with an existing second order method. We also note the level of structural heterogeneity within the brain, and consider the best way to design a sampling protocol. We conclude with a spatial analysis of cell position data, collected using our specified design, from normal birth-weight non S.LD.S., normal birth-weight S.I.D.S and low birth-weight S.LD.S cases.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Statistics