|dc.description.abstract||This thesis evolves around a probabilistic concept called exchangeability and its generalised
forms. It is aimed at exploring connections between exchangeability and other
sub-areas in mathematical statistics. These connections include theoretical implications,
generalisation of existing methodologies and applications to real-world data.
There are three topics of particular interest.
The rst topic is related to the linkage between de Finetti's representation theorem
(for exchangeable sequences) and existence conditions for Hausdor moment problems
over k-dimensional simplexes. The equivalence of these two results are proved over the
most general case in nite spaces. This is a generalisation of existing theory and uses
an alternative approach to previous work in the literature. This connection, while theoretically
interesting in its own right, may also lead to further cross- eld applications,
such as distribution re-construction from nite moments or in the approximations to
nite exchangeable sequences and nite moment problems.
Secondly, we explore a currently popular topic, namely extreme value theory (EVT),
which has been widely applied to areas such as hydrology, earth sciences and nance.
Classical results from EVT assume that the data sequence is independent and identically
distributed (IID). We generalise this assumption to exchangeable random sequences.
This caters for more general approaches to EVT that allows for data dependency.
Resampling techniques are utilised for estimating the parameters' prior distributions.
We utilise these new methods for Value-at-Risk (VaR) estimation in nancial
stock returns. This is done for both cases with and without GARCH lters. These new
VaR models are also compared to existing models in the literature and shows promising
For the nal topic, exchangeability is applied to two-phase sampling with an auxiliary
variable. In particular, our focus is on a two-phase strati ed sampling design, under the
assumption that readings for the study variable are exchangeable within stratum. This
will again provide a generalisation from the usual IID assumption in applications of
multiple-phase sampling. It is amalgamated with stationary bootstrapping at various
levels of sampling to estimate within stratum and cross strata covariances. We show
that our approach provides a more conservative estimate for the sampling variance of
the two-phase estimator for the mean (i.e., the ratio estimator), as compared to the
conventional IID method by Rao (1973)||en_US