Large deviation theory has experienced much development and interest in
the last two decades. A large deviation principle is the exponential decay of the
probability of increasingly rare events and the computation of a rate or entropy
function which measures the rate of decay. Within the probability literature there
has been much use made of these rates in diverse applications. These large
deviation principles have been discovered for independent and identically
distributed random variables, as well as random vectors and these have been
extended to some cases of weak dependence.
In this thesis we prove large deviation principles for finite dimensional
distributions of scaling limits of random measures. Functional approaches to large
deviation theory using test functions as dual objects to random measures are also
developed. These results are applied to some important classes of models, in
particular Poisson point processes, Poisson center cluster processes and doubly
stochastic point processes. / Graduation date: 1991
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/16248 |
| Date | 12 March 1991 |
| Creators | Hwang, Dae-sik |
| Contributors | Burton Jr, Robert M. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0018 seconds