School of Mathematics and Statistics - Theses
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ItemAspects of mixed longitudinal growth analysis(University of Melbourne, 2010)This thesis presents practical approaches to the analysis of mixed longitudinal growth data. Longitudinal studies of the human population are specifically designed to investigate changes over a limited age range in a characteristic which is measured repeatedly for each study participant. This type of data poses several methodological challenges. First, models for the analysis of longitudinal data must recognize the relationship between the observations taken from each study participant. The mixed nature of the data calls for the use of random effects and variance and correlation structures for the within group errors. Secondly, the models must be flexible enough so that they can be easily differentiated for the timing of the population growth spurts. And thirdly, longitudinal growth data of human subjects is more often than not affected by the missing data problem. In practice, the missing data mechanism needs to be understood and taken into consideration when fitting the models. These aspects of mixed longitudinal growth analysis are covered in detail in this thesis using a comprehensive data set of repeated measures of human height of hundreds of Melbourne school children ranging form the ages of 5 to 18 years.
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ItemRisk Analysis and Probabilistic Decision Making for Censored Failure Data( 2019)Operation and maintenance of a fleet always require a high level of readiness, reduced cost, and improved safety. In order to achieve these goals, it is essential to develop and determine an appropriate maintenance programme for the components in use. A failure analysis involving failure model selection, robust parameter estimation, probabilistic decision making, and assessing the cost-effectiveness of the decisions are the key to the selection of a proper maintenance programme. Two significant challenges faced in failure analysis studies are, minimizing the uncertainty associated with model selection and making strategic decisions based on few observed failures. In this thesis, we try to resolve some of these problems and evaluate the cost-effectiveness of the selections. We focus on choosing the best model from a model space and robust estimation of quantiles leading to the selection of optimal repair and replacement time of units. We first explore the repair and replacement cost of a unit in a system. We design a simulation study to assess the performance of the parameter estimation methods, maximum likelihood estimation (MLE), and median rank regression method (MRR) in estimating quantiles of the Weibull distribution. Then, we compare the models; Weibull, gamma, log-normal, log-logistic, and inverse-Gaussian in failure analysis. With an example, we show that the Weibull and the gamma distributions provide competing fits to the failure data. Next, we demonstrate the use of Bayesian model averaging in accounting for that model uncertainty. We derive an average model for the failure observations with respective posterior model probabilities. Then, we illustrate the cost-effectiveness of the selected model by comparing the distribution of the total replacement and repair cost. In the second part of the thesis, we discuss the prior information. Initially, we assume, the parameters of the Weibull distribution are dependent by a function of the form rho = sigma/mu and re-parameterize the Weibull distribution. Then we propose a new Jeffreys’ prior for the parameters mu and rho. Finally, we designed a simulation study to assess the performance of the new Jeffreys’ prior compared to the MLE.
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ItemBiorthogonal Polynomial Sequences and the Asymmetric Simple Exclusion Process( 2019)The diffusion algebra equations of the stationary state of the three parameter Asymmetric Simple Exclusion Process are represented as a linear functional, acting on a tensor algebra. From the linear functional, a pair of sequences (P and Q) of monic polynomials are constructed which are bi-orthogonal, that is, they are orthogonal with respect to each other and not necessarily themselves. The uniqueness and existence of the pair of sequences arises from the determinant of the bi-moment matrix whose elements satisfy a pair of q-recurrence relations. The determinant is evaluated using an LDU-decomposition. If the action of the linear functional is represented as an inner product, then the action of the polynomials Q on a boundary vector V, generates a basis whose orthogonal dual vectors are given by the action of P on the dual boundary vector W}. This basis gives the representation of the algebra which is associated with the Al-Salam-Chihara polynomials obtained by Sasamoto. Several theorems associated with the three parameter asymmetric simple exclusion process are proven combinatorially. The theorems involve the linear functional which, for the three parameter case, is a substitution morphism on a q-Weyl algebra. The two polynomial sequences, P and Q, are represented in terms of q-binomial lattice paths. A combinatorial representation for the value of the linear functional defining the matrix elements of a bi-moment matrix is established in terms of the value of a q-rook polynomial and utilised to provide combinatorial proofs for results pertaining to the linear functional. Combinatorial proofs are provided for theorems in terms of the p,q-binomial coefficients, which are closely related to the combinatorics of the three parameter ASEP. The results for the three parameter diffusion algebra of the Asymmetric Simple Exclusion Process are extended to five parameters. A pair of basis changes are derived from the LDU decomposition of the bi-moment matrix. In order to derive the LDU decomposition a recurrence relation satisfied by the lower triangular matrix elements is conjectured. Associated with this pair of bases are three sequences of orthogonal polynomials. The first pair of orthogonal polynomials generate the new basis vectors (the boundary basis) by their action on the boundary vectors (written is the standard basis), whilst the third orthogonal polynomials are essentially the Askey-Wilson polynomials. All theses results are ultimately related to the LDU decomposition of a matrix.
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ItemStress testing mixed integer programming solvers through new test instance generation methods( 2019)Optimisation algorithms require careful tuning and analysis to perform well in practice. Their performance is strongly affected by algorithm parameter choices, software, and hardware and must be analysed empirically. To conduct such analysis, researchers and developers require high-quality libraries of test instances. Improving the diversity of these test sets is essential to driving the development of well-tested algorithms. This thesis is focused on producing synthetic test sets for Mixed Integer Programming (MIP) solvers. Synthetic data should be carefully designed to be unbiased, diverse with respect to measurable features of instances, have tunable properties to replicate real-world problems, and challenge the vast array of algorithms available. This thesis outlines a framework, methods and algorithms developed to ensure these requirements can be met with synthetically generated data for a given problem. Over many years of development, MIP solvers have become increasingly complex. Their overall performance depends on the interactions of many different components. To cope with this complexity, we propose several extensions over existing approaches to generating optimisation test cases. First, we develop alternative encodings for problem instances which restrict consideration to relevant instances. This approach provides more control over instance features and reduces the computational effort required when we have to resort to search-based generation approaches. Second, we consider more detailed performance metrics for MIP solvers in order to produce test cases which are not only challenging but from which useful insights can be gained. This work makes several key contributions: 1. Performance metrics are identified which are relevant to component algorithms in MIP solvers. This helps to define a more comprehensive performance metric space which looks beyond benchmarking statistics such as CPU time required to solve a problem. Using these more detailed performance metrics we aim to produce explainable and insightful predictions of algorithm performance in terms of instance features. 2. A framework is developed for encoding problem instances to support the design of new instance generators. The concepts of completeness and correctness defined in this framework guide the design process and ensure all problem instances of potential interest are captured in the scheme. Instance encodings can be generalised to develop search algorithms in problem space with the same guarantees as the generator. 3. Using this framework new generators are defined for LP and MIP instances which control feasibility and boundedness of the LP relaxation, and integer feasibility of the resulting MIP. Key features of the LP relaxation solution, which are directly controlled by the generator, are shown to affect problem difficulty in our analysis of the results. The encodings used to control these properties are extended into problem space search operators to generate further instances which discriminate between solver configurations. This work represents the early stages of an iterative methodology required to generate diverse test sets which continue to challenge the state of the art. The framework, algorithms and codes developed in this thesis are intended to support continuing development in this area.
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ItemIntelligent Management of Elective Surgery Patient Flow( 2019)Rapidly growing demand and soaring costs for healthcare services in Australia and across the world are jeopardising the sustainability of government-funded healthcare systems. We need to be innovative and more efficient in delivering healthcare services in order to keep the system sustainable. In this thesis, we utilise a number of scientific tools to improve the patient flow in a surgical suite of a hospital and subsequently develop a structured approach for intelligent patient flow management. First, we analyse and understand the patient flow process in a surgical suite. Then we obtain data from the partner hospital and extract valuable information from a large database. Next, we use machine learning techniques, such as classification and regression tree analysis, random forest, and k-nearest neighbour regression, to classify patients into lower variability resource user groups and fit discrete phase-type distributions to the clustered length of stay data. We use length of stay scenarios sampled from the fitted distributions in our sequential stochastic mixed-integer programming model for tactical master surgery scheduling. Our mixed-integer programming model has the particularity that the scenarios are utilised in a chronologically sequential manner, not in parallel. Moreover, we exploit the randomness in the sample path to reduce the requirement of optimising the process for many scenarios which helps us obtain high-quality schedules while keeping the problem algorithmically tractable. Last, we model the patient flow process in a healthcare facility as a stochastic process and develop a model to predict the probability of the healthcare facility exceeding capacity the next day as a function of the number of inpatients and the next day scheduled patients, their resource user groups, and their elapsed length of stay. We evaluate the model's performance using the receiver operating characteristic curve and illustrate the computation of the optimal threshold probability by using cost-benefit analysis that helps the hospital management make decisions.
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ItemCopula-based spatio-temporal modelling for count data( 2019)Modelling of spatio-temporal count data has received considerable attention in recent statistical research. However, the presence of massive correlation between locations, time points and variables imposes a great computational challenge. In existing literature, latent models under the Bayesian framework are predominately used. Despite numerous theoretical and practical advantages, likelihood analysis of spatio-temporal modelling on count data is less wide spread, due to the difficulty in identifying the general class of multivariate distributions for discrete responses. In this thesis, we propose a Gaussian copula regression model (copSTM) for the analysis of multivariate spatio-temporal data on lattice. Temporal effects are modelled through the conditional marginal expectations of the response variables using an observation-driven time series model, while spatial and cross-variable correlations are captured in a block dependence structure, allowing for both positive and negative correlations. The proposed copSTM model is flexible and sufficiently generalizable to many situations. We provide pairwise composite likelihood inference tools. Numerical examples suggest that the proposed composite likelihood estimator produces satisfactory estimation performance. While variable selection of generalized linear models is a well developed topic, model subsetting in applications of Gaussian copula models remains a relatively open research area. The main reason is the computational burden that is already quite heavy for simply fitting the model. It is therefore not computationally affordable to evaluate many candidate sub-models. This makes penalized likelihood approaches extremely inefficient because they need to search through different levels of penalty strength, apart from the fact suggested by our numerical experience that optimization of penalized composite likelihoods with many popular penalty terms (e.g LASSO and SCAD) usually does not converge in copula models. Thus, we propose to use a criterion-based selection approach that borrows strength from the Gibbs sampling technique.The methodology guarantees to converge to the model with the lowest criterion value, yet without searching through all possible models exhaustively. Finally, we present an R package implementing the estimation and selection of the copSTM model in C++. We show examples comparing our package to many available R packages (on some special cases of the copSTM), confirming the correctness and efficiency of the package functions. The package copSTM provides a competitive toolkit option for the analysis spatio-temporal count data on lattice in terms of both model flexibility and computational efficiency.
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ItemSingular vectors for the WN algebras and the BRST cohomology for relaxed highest-weight Lk(sl(2)) modules( 2019)This thesis presents the computation of singular vectors of the W_n algebras and the BRST cohomology of modules of the simple vertex operator algebra L_k(sl2) associated to the affine Lie algebra of sl2 in the relaxed category We will first recall some general theory on vertex operator algebras. We will then introduce the module categories that are relevant for conformal field theory. They are the category O of highest-weight modules and the relaxed category which contains O as well as the relaxed highest-weight modules with spectral flow and non-split extensions. We will then introduce the W_n algebras and the simple vertex operator algebra L_k(sl2). Properties of the Heisenberg algebra, the bosonic and the fermionic ghosts will be discussed as they are required in the free field realisations of W_n and L_k(sl2) as well as the construction of the BRST complex. We will then compute explicitly the singular vectors of W_n algebras in their Fock representations. In particular, singular vectors can be realised as the image of screening operators of the W_n algebras. One can then realise screening operators in terms of Jack functions when acting on a highest-weight state, thereby obtaining explicit formulae of the singular vectors in terms of symmetric functions. We will then discuss the BRST construction and the BRST cohomology for modules in category O. Lastly we compute the BRST cohomology for L_k(sl2) modules in the relaxed category. In particular, we compute the BRST cohomology for the highest-weight modules with positive spectral flow for all degrees and the BRST cohomology for the highest-weight modules with negative spectral flow for one degree.
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ItemMissing data analysis, combinatorial model selection and structure learning( 2019)This thesis examines three problems in statistics: the missing data problem in the context of extracting trends from time series data, the combinatorial model selection problem in regression analysis, and the structure learning problem in graphical modelling / system identification. The goal of the first problem is to study how uncertainty in the missing data affects trend extraction. This work derives an analytical bound to characterise the error of the estimated trend in terms of the error of the imputation. It works for any imputation method and various trend-extraction methods, including a large subclass of linear filters and the Seasonal-Trend decomposition based on Loess (STL). The second problem is to tackle the combinatorial complexity which arises from the best-subset selection in regression analysis. Given p variables, a model can be formed by taking a subset of the variables, and the total number of models p is $2^p$. This work shows that if a hierarchical structure can be established on the model space, then the proposed algorithm, Gibbs Stochastic Search (GSS), can recover the true model with probability one in the limit and high probability with finite samples. The core idea is that when a hierarchical structure exists, every evaluation of a wrong model would give information about the correct model. By aggregating these information, one may recover the correct model without exhausting the model space. As an extension, parallelisation of the algorithm is also considered. The third problem is about inferring from data the systemic relationship between a set of variables. This work proposes a flexible class of multivariate distributions in a form of a directed acyclic graphical model, which uses a graph and models each node conditioning on the rest using a Generalised Linear Model (GLM), and it shows that while the number of possible graphs is $\Omega(2^{p \choose 2})$, a hierarchical structure exists and the GSS algorithm applies. Hence, a systemic relationship may be recovered from the data. Other applications like imputing missing data and simulating data with complex covariance structure are also investigated.
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ItemExact solutions in multi-species exclusion processes( 2019)The exclusion process has been the default model for the transportation phenomenon. One fundamental issue is to compute the exact formulae analytically. Such formulae enable us to obtain the limiting distribution through asymptotics analysis, and they also allow us to uncover relationships between different processes, and even between very different systems. Extensive results have been reported for single-species systems, but few for multi-component systems and mixtures. In this thesis, we focus on multi-species exclusion processes, and propose two approaches for exact solutions. The first one is due to duality, which is defined by a function that co-varies in time with respect to the evolution of two processes. It relates physical quantities, such as the particle flow, in a system with many particles to one with few particles, so that the quantity of interest in the first process can be calculated explicitly via the second one. Historically, published dualities have mostly been found by trial and error. Only very recently have attempts been made to derive these functions algebraically. We propose a new method to derive dualities systematically, by exploiting the mathematical structure provided by the deformed quantum Knizhnik-Zamolodchikov equation. With this method, we not only recover the well-known self-duality in single-species asymmetric simple exclusion processes (ASEPs), and also obtain the duality for two-species ASEPs. Solving the master equation is an alternative method. We consider an exclusion process with 2 species particles: the AHR (Arndt-Heinzl-Rittenberg) model and give a full derivation of its Green's function via coordinate Bethe ansatz. Hence using the Green's function, we obtain an integral formula for its joint current distributions, and then study its limiting distribution with step type initial conditions. We show that the long-time behaviour is governed by a product of the Gaussian and the Gaussian unitary ensemble (GUE) Tracy-Widom distributions, which is related to the random matrix theory. Such result agrees with the prediction made by the nonlinear fluctuating hydrodynamic theory (NLFHD). This is the first analytic verification of the prediction of NLFHD in a multi-species system.
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ItemYang-Baxter integrable dimers and fused restricted-solid-on-solid lattice models( 2019)The main objects of investigation in this thesis are two Yang-Baxter integrable lattice models of statistical mechanics in two dimensions: nonunitary RSOS models and dimers. At criticality they admit continuum descriptions with nonunitary conformal field theories (CFTs) in (1+1) dimensions. CFTs are quantum field theory invariant under conformal transformations. They play a major role in the theory of phase transition and critical phenomena. In quantum field theory unitarity is the requirement that the probability is conserved, hence realistic physical problems are associated with unitary quantum field theories. Nevertheless, in statistical mechanics this property loses a physical meaning and statistical systems like polymers and percolations, which model physical problems with long-range interactions, in the continuum scaling limit give rise to nonunitary conformal field theories. Both the nonunitary RSOS models and dimers are defined on a two-dimensional square lattice. Restricted solid-on-solid (RSOS) models are so called because their degrees of freedom are in the form of a finite (therefore restricted) set of heights which live on the sites of the lattice and their interactions take place between the four sites around each face of the lattice (solid-on-solid). Each allowed configuration of heights maps to a specific Boltzmann weight. RSOS are integrable in the sense that their Boltzmann weights and transfer matrices satisfy the Yang-Baxter equation. The CFTs associated to critical RSOS models are minimal models, the simplest family of rational conformal field theories. The process of fusion on elementary RSOS models has a different outcome on the CFT side depending on both the level of fusion and the value of their crossing parameter λ. Precisely, in the interval 0 < λ < π/2, the 2x2 fused RSOS models correspond to higher-level conformal cosets with integer level of fusion equal to two. Instead in the complementary interval π/n < λ < π the 2x2 fused RSOS models are related to minimal models with integer level of fusion equal to one. To prove this conjecture one-dimensional sums, deriving from the well-known Yang-Baxter corner transfer matrix method, have been calculated, extended in the continuum limit and ultimately compared to the conformal characters of the related minimal models. The dimer model has been for a long time object of various scientific studies, firstly as simple prototype of diatomic molecules and then as equivalent formulation to the well-known domino tilings problem of statistical mechanics. However, only more recently it has attracted attention as conformal field theory thanks to its relation with another famous integrable lattice model, the six-vertex model. What is particularly interesting of dimers is the property of being a free-fermion model and at the same time showing non-local properties due to the long-range steric effects propagating from the boundaries. This non-locality translates then in the dependance of their bulk free energy on the boundary conditions. We formulate the dimer model as a Yang-Baxter integrable free-fermion six-vertex model. This model is integrable in different geometries (cylinder, torus and strip) and with a variety of different integrable boundary conditions. The exact solution for the partition function arises from the complete spectra of eigenvalues of the transfer matrix. This is obtained by solving some functional equations, in the form of inversion identities, usually associated to the transfer matrix of the free-fermion six-vertex model, and using the physical combinatorics of the pattern of zeros of the transfer matrix eigenvalues to classify the eigenvalues according to their degeneracies. In the case of the cylinder and torus, the transfer matrix can be diagonalized, while, in the other cases, we observe that in a certain representation the double row transfer matrix exhibits non trivial Jordan-cells. Remarkably, the spectrum of eigenvalues of dimers and critical dense polymers agree sectors by sectors. The similarity with critical dense polymers, which is a logarithmic field theory, raises the question whether also the free-fermion dimer model manifests a logarithmic behaviour in the continuum scaling limit. The debate is still open. However, in our papers we provide a final answer and argue that the type of conformal field theory which best describe dimers is a logarithmic field theory, as it results by looking at the numerically estimate of the finite size corrections to the critical free energy of the free-fermion six-vertex-equivalent dimer model. The thesis is organized as follows. The first chapter is an introduction which has the purpose to inform the reader about the basics of statistical mechanics, from one side, and CFTs, on the other side, with a specific focus on the two lattice models that have been studied (nonunitary RSOS and dimers) and the theories associated to the their continuum description at criticality (minimal models and logarithmic CFTs). The second chapter considers the family of non-unitary RSOS models with π/n < λ < π and brings forward the discussion around the one-dimensional sums of the elementary and fused models, and the associated conformal characters in the continuum scaling limit. The third and fourth chapters are dedicated to dimers, starting with periodic conditions on a cylinder and torus, and then more general integrable boundary conditions on a strip. In each case, a combinatorial analysis of the pattern of zeros of the transfer matrix eigenvalues is presented and extensively treated. It follows then the analysis of the finite-size corrections to the critical free energy. Finally, the central charges and minimal conformal dimensions of critical dimers are discussed in depth with concluding remarks about the logarithmic hypothesis. Next, there is a conclusion where the main results of these studies are summarized and put into perspective with possible future research goals.