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People matching "Statistical computing"
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Associate Professor Zudi Lu Associate Professor in Statistics and ARC Future Fellow
More about Zudi Lu...
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Courses matching "Statistical computing"
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Advanced statistical inference 
We begin with modern and classical statistical inference and cover cumulants, the cumulant
generating function, natural exponential family models, minimal sufficient statistics, completeness,
and generalised linear models. We then consider conditional and marginal inference including the
concept of ancillary statistics, marginal likelihood and conditional inference. Chapter 2 is about model
choice, in particular Akaike's Information Criterion (AIC), Network Information Criterion (NIC), and
cross-validation (CV). We will explore the theoretical basis of AIC via model misspecification and the
Kullback-Leibler distance. Chapter 3 is devoted to bootstrap methods for assessing statistical
accuracy; we will focus on bootstrap estimation and confidence intervals, and consider the jackknife
and its relationship to the bootstrap. Chapter 4 is on the analysis of missing data; we will study the
different types of missingness and the Expectation-Maximisation (EM) algorithm in particular. Chapter
5 is about survival analysis, and we will cover the Kaplan-Meier estimator, parametric survival models,
and the semi-parametric proportional hazards model.
More about this course...
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Mathematical epidemiology: Stochastic models and their statistical calibration
Mathematical models are increasingly used to inform governmental policy-makers on issues that
threaten human health or which have an adverse impact on the economy. It is this real-world success
combined with the wide variety of interesting mathematical problems which arise that makes
mathematical epidemiology one of the most exciting topics in applied mathematics. During the
summer school, you will be introduced to mathematical epidemiology and some fundamental theory
required for studying and parametrising stochastic models of infection dynamics, which will provide an
ideal basis for addressing key research questions in this area; several such questions will be
introduced and explored in this course. Topics:
An introduction to mathematical epidemiology
Discrete-time and continuous-time discrete-state stochastic infection models
Numerical methods for studying stochastic infection models: EXPOKIT, transforms and their inversion
Methods for simulating stochastic infection models: classical (Gillespie) algorithm, more efficient exact
and approximate algorithms
Methods for parameterising stochastic infection models: frequentist approaches, Bayesian
approaches, approximate Bayesian computation
Optimal observation of stochastic infection models
More about this course...
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Statistical Analysis and Modelling 1
This is a first course in Statistics for mathematically inclined students. It will address the key principles underlying commonly used statistical methods such as confidence intervals, hypothesis tests, inference for means and proportions, and linear regression. It will develop a deeper mathematical understanding of these ideas, many of which will be familiar from studies in secondary school. The application of basic and more advanced statistical methods will be illustrated on a range of problems from areas such as medicine, science, technology, government, commerce and manufacturing. The use of the statistical package SPSS will be developed through a sequence of computer practicals. Topics covered will include: basic probability and random variables, fundamental distributions, inference for means and proportions, comparison of independent and paired samples, simple linear regression, diagnostics and model checking, multiple linear regression, simple factorial models, models with factors and continuous predictors.
More about this course...
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Statistical Modelling and Inference
Statistical methods are important to all areas that rely on data including science, technology, government and commerce. To deal with the complex problems that arise in practice requires a sound understanding of fundamental statistical principles together with a range of suitable modelling techniques. Computing using a high level statistical package is also an essential element of modern statistical practice. This course provides an introduction to the principles of statistical inference and the development of linear statistical models with the statistical package R. Topics covered are: Point estimates, unbiasedness, mean-squared error, confidence intervals, tests of hypotheses, power calculations, derivation of one and two-sample procedures; simple linear regression, regression diagnostics, prediction; linear models, ANOVA, multiple regression, factorial experiments, analysis of covariance models, model building; likelihood based methods for estimation and testing, goodness of fit tests; sample surveys, population means, totals and proportions, simple random samples, stratified random samples. Topics covered are: point estimates, unbiasedness, mean-squared error, confidence intervals, tests of hypotheses, power calculations, derivation of one and two-sample procedures: simple linear regression, regression diagnostics, prediction: linear models, analysis of variance (ANOVA), multiple regression, factorial experiments, analysis of covariance models, model building; likelihood-based methods for estimation and testing and goodness-of-fit tests.
More about this course...
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Statistical Modelling III
One of the key requirements of an applied statistician is the ability to formulate appropriate statistical models and then apply them to data in order to answer the questions of interest. Most often, such models can be seen as relating a response variable to one or more explanatory variables. For example, in a medical experiment we may seek to evaluate a new treatment by relating patient outcome to treatment received while allowing for background variables such as age, sex and disease severity. In this course, a rigorous discussion of the linear model is given and various extensions are developed. There is a strong practical emphasis and the statistical package R is used extensively. Topics covered are: the linear model, least squares estimation, generalised least squares estimation, properties of estimators, the Gauss-Markov theorem; geometry of least squares, subspace formulation of linear models, orthogonal projections; regression models, factorial experiments, analysis of covariance and model formulae; regression diagnostics, residuals, influence diagnostics, transformations, Box-Cox models, model selection and model building strategies; models with complex error structure, split-plot experiments; logistic regression models.
More about this course...
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Statistical Practice I
Statistical ideas and methods are essential tools in virtually all areas that rely on data to make decisions and reach conclusions. This includes diverse fields such as medicine, science, technology, government, commerce and manufacturing. In broad terms, statistics is about getting information from data. This includes both the important question of how to obtain suitable data for a given purpose and also how best to extract the information, often in the presence of random variability. This course provides an introduction to the contemporary application of statistics to a wide range of real world situations. It has a strong practical focus using the statistical package SPSS to analyse real data. Topics covered are: organisation, description and presentation of data; design of experiments and surveys; random variables, probability distributions, the binomial distribution and the normal distribution; statistical inference, tests of significance, confidence intervals; inference for means and proportions, one-sample tests, two independent samples, paired data, t-tests, contingency tables; analysis of variance; linear regression, least squares estimation, residuals and transformations, inference for regression coefficients, prediction.
More about this course...
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Statistical Practice I (Life Sciences)
Statistical ideas and methods are essential tools in virtually all areas that rely on data to make decisions and reach conclusions. This includes diverse fields such as science, technology, government, commerce, manufacturing and the life sciences. In broad terms, statistics is about getting information from data. This includes both the important question of how to obtain suitable data for a given purpose and also how best to extract the information, often in the presence of random variability. This course provides an introduction to the contemporary application of statistics to a range of real world situations. It has a strong practical focus using the statistical package SPSS to analyse real data relevant to the life sciences. Topics covered are: organisation, description and presentation of data in the life sciences; design of experiments and surveys; random variables, probability distributions, the binomial distribution and the normal distribution; statistical inference, tests of significance, confidence intervals; inference for means and proportions, one-sample tests, two independent samples, paired data, t-tests, contingency tables; analysis of variance; linear regression, least squares estimation, residuals and transformations, inference for regression coefficients, prediction.
More about this course...
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Statistical Practice I (Life Sciences) (Pre-Vet)
Statistical ideas and methods are essential tools in virtually all areas that rely on data to make decisions and reach conclusions. This includes diverse fields such as science, technology, government, commerce, manufacturing and the life sciences. In broad terms, statistics is about getting information from data. This includes both the important question of how to obtain suitable data for a given purpose and also how best to extract the information, often in the presence of random variability. This course provides an introduction to the contemporary application of statistics to a range of real world situations. It has a strong practical focus using the statistical package SPSS to analyse real data relevant to the life sciences. Topics covered are: organisation, description and presentation of data in the life sciences; design of experiments and surveys; random variables, probability distributions, the binomial distribution and the normal distribution; statistical inference, tests of significance, confidence intervals; inference for means and proportions, one-sample tests, two independent samples, paired data, t-tests, contingency tables; analysis of variance; linear regression, least squares estimation, residuals and transformations, inference for regression coefficients, prediction.
More about this course...
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Events matching "Statistical computing"
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Statistical convergence of sequences of complex numbers with application to Fourier series 15:10 Tue 27 Mar 07 :: G08 Mathematics Building University of Adelaide :: Prof. Ferenc Morics
Media...
The concept of statistical convergence was introduced by Henry Fast and Hugo Steinhaus in 1951. But in fact, it was Antoni Zygmund who first proved theorems on the statistical convergence of Fourier series, using the term \"almost convergence\". A sequence $\\{x_k : k=1,2\\ldots\\}$ of complex numbers is said to be statistically convergent to $\\xi$ if for every $\\varepsilon >0$ we have $$\\lim_{n\\to \\infty} n^{-1} |\\{1\\le k\\le n: |x_k-\\xi| > \\varepsilon\\}| = 0.$$ We present the basic properties of statistical convergence, and extend it to multiple sequences. We also discuss the convergence behavior of Fourier series.
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Statistical Critique of the International Panel on Climate Change's work on Climate Change. 18:00 Wed 17 Oct 07 :: Union Hall University of Adelaide :: Mr Dennis Trewin
Climate change is one of the most important issues facing us today. Many governments have introduced or are developing appropriate policy interventions to (a) reduce the growth of greenhouse gas emissions in order to mitigate future climate change, or (b) adapt to future climate change.
This important work deserves a high quality statistical data base but there are statistical shortcomings in the work of the International Panel on Climate Change (IPCC). There has been very little involvement of qualified statisticians in the very important work of the IPCC which appears to be scientifically meritorious in most other ways.
Mr Trewin will explain these shortcomings and outline his views on likely future climate change, taking into account the statistical deficiencies.
His conclusions suggest climate change is still an important issue that needs to be addressed but the range of likely outcomes is a lot lower than has been suggested by the IPCC.
This presentation will be based on an invited paper presented at the OECD World Forum.
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Moderated Statistical Tests for Digital Gene Expression Technologies 15:10 Fri 19 Oct 07 :: G04 Napier Building University of Adelaide :: Dr Gordon Smyth :: Walter and Eliza Hall Institute of Medical Research in Melbourne, Australia
Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of DNA sequencing decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using over-dispersed binomial or Poisson models for the counts, but none of the these are usable when the number of replicates is very small. We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. A heuristic empirical Bayes algorithm is developed which is applicable to very general likelihood estimation contexts. Not only is our strategy applicable even with the smallest number of replicates, but it also proves to be more powerful than previous strategies when more replicates are available. The methodology is applicable to other counting technologies, such as proteomic spectral counts.
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Statistical analysis for harmonized development of systemic organs in human fetuses 11:00 Thu 17 Sep 09 :: School Board Room :: Prof Kanta Naito :: Shimane University
The growth processes of human babies have been studied
sufficiently in scientific fields, but there have still been many issues
about the developments of human fetus which are not clarified. The aim of
this research is to investigate the developing process of systemic organs of
human fetuses based on the data set of measurements of fetus's bodies and
organs. Specifically, this talk is concerned with giving a mathematical
understanding for the harmonized developments of the organs of human
fetuses. The method to evaluate such harmonies is proposed by the use of the
maximal dilatation appeared in the theory of quasi-conformal mapping.
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Stable commutator length 13:40 Fri 25 Sep 09 :: Napier 102 :: Prof Danny Calegari :: California Institute of Technology
Stable commutator length answers the question: "what is the simplest
surface in a given space with prescribed boundary?" where "simplest"
is interpreted in topological terms. This topological definition is
complemented by several equivalent definitions - in group theory, as a
measure of non-commutativity of a group; and in linear programming, as
the solution of a certain linear optimization problem. On the
topological side, scl is concerned with questions such as computing
the genus of a knot, or finding the simplest 4-manifold that bounds a
given 3-manifold. On the linear programming side, scl is measured in
terms of certain functions called quasimorphisms, which arise from
hyperbolic geometry (negative curvature) and symplectic geometry
(causal structures). In these talks we will discuss how scl in free
and surface groups is connected to such diverse phenomena as the
existence of closed surface subgroups in graphs of groups, rigidity
and discreteness of symplectic representations, bounding immersed
curves on a surface by immersed subsurfaces, and the theory of multi-
dimensional continued fractions and Klein polyhedra.
Danny Calegari is the Richard Merkin Professor of Mathematics at the California Institute of Technology, and is one of the recipients of the 2009 Clay Research Award for his work in geometric topology and geometric group theory. He received a B.A. in 1994 from the University of Melbourne, and a Ph.D. in 2000 from the University of California, Berkeley under the joint supervision of Andrew Casson and William Thurston. From 2000 to 2002 he was Benjamin Peirce Assistant Professor at Harvard University, after which he joined the Caltech faculty; he became Richard Merkin Professor in 2007.
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Contemporary frontiers in statistics 15:10 Mon 28 Sep 09 :: Badger Labs G31 Macbeth Lectrue :: Prof. Peter Hall :: University of Melbourne
The availability of powerful computing equipment has had a dramatic impact on statistical methods and thinking, changing forever the way data are analysed. New data types, larger quantities of data, and new classes of research problem are all motivating new statistical methods. We shall give examples of each of these issues, and discuss the current and future directions of frontier problems in statistics.
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Manifold destiny: a talk on water, fire and life 15:10 Fri 6 Nov 09 :: MacBeth Lecture Theatre :: Dr Sanjeeva Balasuriya :: University of Adelaide
Manifolds are important entities in dynamical systems, and organise space
into regions in which different motions occur. For example, intersections
between stable and unstable manifolds in discrete systems result in
chaotic motion. This talk will focus on manifolds and their locations in
continuous dynamical systems, and in particular on Melnikov's method and its adaptations for determining the effect of perturbations on manifolds.
The relevance of such adaptations to a surprising range of applications will be shown, in addition to recent theoretical developments inspired by such problems. The applications addressed in this talk include understanding the motion of fluid near oceanic eddies and currents, optimising mixing in nano-fluidic devices in order to improve reactions, computing the speed of a flame front, and finding the spreading rate of bacterial colonies.
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Exploratory experimentation and computation 15:10 Fri 16 Apr 10 :: Napier LG29 :: Prof Jonathan Borwein :: University of Newcastle
Media...
The mathematical research community is facing a great challenge to re-evaluate the role of proof in light of the growing power of current computer systems, of modern mathematical computing packages, and of the growing capacity to data-mine on the Internet. Add to that the enormous complexity of many modern capstone results such as the Poincare conjecture, Fermat's last theorem, and the Classification of finite simple groups. As the need and prospects for inductive mathematics blossom, the requirement to ensure the role of proof is properly founded remains undiminished. I shall look at the philosophical context with examples and then offer some of five bench-marking examples of the opportunities and challenges we face.
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Mathematica Seminar 15:10 Wed 28 Jul 10 :: Engineering Annex 314 :: Kim Schriefer :: Wolfram Research
The Mathematica Seminars 2010 offer an opportunity to experience the applicability, ease-of-use, as well as the advancements of Mathematica 7 in education and academic research. These seminars will highlight the latest directions in technical computing with Mathematica, and the impact this technology has across a wide range of academic fields, from maths, physics and biology to finance, economics and business.
Those not yet familiar with Mathematica will gain an overview of the system and discover the breadth of applications it can address, while experts will get firsthand experience with recent advances in Mathematica like parallel computing, digital image processing, point-and-click palettes, built-in curated data, as well as courseware examples.
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A spatial-temporal point process model for fine resolution multisite rainfall data from Roma, Italy 14:10 Thu 19 Aug 10 :: Napier G04 :: A/Prof Paul Cowpertwait :: Auckland University of Technology
A point process rainfall model is further developed that has storm origins occurring in space-time according to a Poisson process. Each storm origin has a random radius so that storms occur as circular regions in two-dimensional
space, where the storm radii are taken to be independent exponential random
variables. Storm origins are of random type z, where z follows a continuous
probability distribution. Cell origins occur in a further spatial Poisson
process and have arrival times that follow a Neyman-Scott point process. Cell
origins have random radii so that cells form discs in two-dimensional space.
Statistical properties up to third order are derived and used to fit the model
to 10 min series taken from 23 sites across the Roma region, Italy.
Distributional properties of the observed annual maxima are compared to
equivalent values sampled from series that are simulated using the fitted
model. The results indicate that the model will be of use in urban drainage
projects for the Roma region.
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Statistical physics and behavioral adaptation to Creation's main stimuli: sex and food 15:10 Fri 29 Oct 10 :: E10 B17 Suite 1 :: Prof Laurent Seuront :: Flinders University and South Australian Research and Development Institute
Animals typically search for food and mates, while avoiding predators. This is particularly critical for keystone organisms such as intertidal gastropods and copepods (i.e. millimeter-scale crustaceans) as they typically rely on non-visual senses for detecting, identifying and locating mates in their two- and three-dimensional environments. Here, using stochastic methods derived from the field of nonlinear physics, we provide new insights into the nature (i.e. innate vs. acquired) of the motion behavior of gastropods and copepods, and demonstrate how changes in their behavioral properties can be used to identify the trade-offs between foraging for food or sex. The gastropod Littorina littorea hence moves according to fractional Brownian motions while foraging for food (in accordance with the fractal nature of food distributions), and switch to Brownian motion while foraging for sex. In contrast, the swimming behavior of the copepod Temora longicornis belongs to the class of multifractal random walks (MRW; i.e. a form of anomalous diffusion), characterized by a nonlinear moment scaling function for distance versus time. This clearly differs from the traditional Brownian and fractional Brownian walks expected or previously detected in animal behaviors. The divergence between MRW and Levy flight and walk is also discussed, and it is shown how copepod anomalous diffusion is enhanced by the presence and concentration of conspecific water-borne signals, and is dramatically increasing male-female encounter rates.
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Statistical challenges in molecular phylogenetics 15:10 Fri 20 May 11 :: Mawson Lab G19 lecture theatre :: Dr Barbara Holland :: University of Tasmania
Media...
This talk will give an introduction to the ways that mathematics and statistics gets used in the inference of evolutionary (phylogenetic) trees. Taking a model-based approach to estimating the relationships between species has proven to be an enormously effective, however, there are some tricky statistical challenges that remain. The increasingly plentiful amount of DNA sequence data is a boon, but it is also throwing a spotlight on some of the shortcomings of current best practice particularly in how we (1) assess the reliability of our phylogenetic estimates, and (2) how we choose appropriate models. This talk will aim to give a general introduction this area of research and will also highlight some results from two of my recent PhD students.
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Statistical modelling in economic forecasting: semi-parametrically spatio-temporal approach 12:10 Mon 23 May 11 :: 5.57 Ingkarni Wardli :: Dawlah Alsulami :: University of Adelaide
How to model spatio-temporal variation of housing prices is an important and challenging problem as it is of vital importance for both investors and policy makersto assess any movement in housing prices. In this seminar I will talk about the proposed model to estimate any movement in housing prices and measure the risk more accurately.
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Routing in equilibrium 15:10 Tue 21 Jun 11 :: 7.15 Ingkarni Wardli :: Dr Timothy Griffin :: University of Cambridge
Media...
Some path problems cannot be modelled
using semirings because the associated
algebraic structure is not distributive. Rather
than attempting to compute globally optimal
paths with such structures, it may be sufficient
in some cases to find locally optimal paths ---
paths that represent a stable local equilibrium.
For example, this is the type of routing system that
has evolved to connect Internet Service Providers
(ISPs) where link weights implement
bilateral commercial relationships between them.
Previous work has shown that routing equilibria can
be computed for some non-distributive algebras
using algorithms in the Bellman-Ford family.
However, no polynomial time bound was known
for such algorithms. In this talk, we show that
routing equilibria can be computed using
Dijkstra's algorithm for one class of non-distributive
structures. This provides the first
polynomial time algorithm for computing locally
optimal solutions to path problems.
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Statistical analysis of metagenomic data from the microbial community involved in industrial bioleaching 12:10 Mon 19 Sep 11 :: 5.57 Ingkarni Wardli :: Ms Susana Soto-Rojo :: University of Adelaide
In the last two decades heap bioleaching has become established as a successful commercial option for recovering copper from low-grade secondary sulfide ores. Genetics-based approaches have recently been employed in the task of characterizing mineral processing bacteria. Data analysis is a key issue and thus the implementation of adequate mathematical and statistical tools is of fundamental importance to draw reliable conclusions. In this talk I will give a recount of two specific problems that we have been working on. The first regarding experimental design and the latter on modeling composition and activity of the microbial consortium.
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Statistical analysis of school-based student performance data 12:10 Mon 10 Oct 11 :: 5.57 Ingkarni Wardli :: Ms Jessica Tan :: University of Adelaide
Join me in the journey of being a statistician for 15 minutes of your day (if you are not already one) and experience the task of data cleaning without having to get your own hands dirty. Most of you may have sat the Basic Skills Tests when at school or know someone who currently has to do the NAPLAN (National Assessment Program - Literacy and Numeracy) tests. Tests like these assess student progress and can be used to accurately measure school performance. In trying to answer the research question: "what conclusions about student progress and school performance can be drawn from NAPLAN data or data of a similar nature, using mathematical and statistical modelling and analysis techniques?", I have uncovered some interesting results about the data in my initial data analysis which I shall explain in this talk.
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Statistical modelling for some problems in bioinformatics 11:10 Fri 14 Oct 11 :: B.17 Ingkarni Wardli :: Professor Geoff McLachlan :: The University of Queensland
Media...
In this talk we consider some statistical analyses of data arising in
bioinformatics. The problems include the detection of differential
expression in microarray gene-expression data, the clustering of
time-course gene-expression data and, lastly, the analysis of
modern-day cytometric data. Extensions are considered to the procedures
proposed for these three problems in McLachlan et al. (Bioinformatics, 2006),
Ng et al. (Bioinformatics, 2006), and Pyne et al. (PNAS, 2009), respectively.
The latter references are available at http://www.maths.uq.edu.au/~gjm/.
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Principal Component Analysis (PCA) 12:30 Mon 3 Sep 12 :: B.21 Ingkarni Wardli :: Mr Lyron Winderbaum :: University of Adelaide
Media...
Principal Component Analysis (PCA) has become something of a buzzword recently in a number of disciplines including the gene expression and facial recognition. It is a classical, and fundamentally simple, concept that has been around since the early 1900's, its recent popularity largely due to the need for dimension reduction techniques in analyzing high dimensional data that has become more common in the last decade, and the availability of computing power to implement this. I will explain the concept, prove a result, and give a couple of examples. The talk should be accessible to all disciplines as it (should?) only assume first year linear algebra, the concept of a random variable, and covariance.
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News matching "Statistical computing"
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Usenet Conference Associate Professor Matt Roughan (Applied Mathematics) has been invited to Co-Chair the Association for Computing Machinery Usenet Internet Measurement Conference. Posted Mon 15 Jan 07.
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New Professor of Statistical Bioinformatics Associate Professor Patty Solomon will take up the Chair of Statistical Bioinformatics within the School of Mathematical Sciences effective from 29th of October, 2007. Posted Mon 29 Oct 07.
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ARC Grant successes The School of Mathematical Sciences has again had outstanding success in the ARC Discovery and Linkage Projects schemes.
Congratulations to the following staff for their success in the Discovery Project scheme:
Prof Nigel Bean, Dr Josh Ross, Prof Phil Pollett, Prof Peter Taylor, New methods for improving active adaptive management in biological systems, $255,000 over 3 years;
Dr Josh Ross, New methods for integrating population structure and stochasticity into models of disease dynamics, $248,000 over three years;
A/Prof Matt Roughan, Dr Walter Willinger, Internet traffic-matrix synthesis, $290,000 over three years;
Prof Patricia Solomon, A/Prof John Moran, Statistical methods for the analysis of critical care data, with application to the Australian and New Zealand Intensive Care Database, $310,000 over 3 years;
Prof Mathai Varghese, Prof Peter Bouwknegt, Supersymmetric quantum field theory, topology and duality, $375,000 over 3 years;
Prof Peter Taylor, Prof Nigel Bean, Dr Sophie Hautphenne, Dr Mark Fackrell, Dr Malgorzata O'Reilly, Prof Guy Latouche, Advanced matrix-analytic methods with applications, $600,000 over 3 years.
Congratulations to the following staff for their success in the Linkage Project scheme:
Prof Simon Beecham, Prof Lee White, A/Prof John Boland, Prof Phil Howlett, Dr Yvonne Stokes, Mr John Wells, Paving the way: an experimental approach to the mathematical modelling and design of permeable pavements, $370,000 over 3 years;
Dr Amie Albrecht, Prof Phil Howlett, Dr Andrew Metcalfe, Dr Peter Pudney, Prof Roderick Smith, Saving energy on trains - demonstration, evaluation, integration, $540,000 over 3 years
Posted Fri 29 Oct 10.
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Publications matching "Statistical computing"
| Publications |
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Adaptively varying-coefficient spatiotemporal models
Lu, Zudi; Steinskog, D; Tjostheim, D; Yao, Q, Journal of the Royal Statistical Society Series B-Statistical Methodology 71 (859–880) 2009 |
Algorithms for the Laplace-Stieltjes transforms of first return times for stochastic fluid flows
Bean, Nigel; O'Reilly, Malgorzata; Taylor, Peter, Methodology and Computing in Applied Probability 10 (381–408) 2008 |
Robust Optimal Portfolio Choice Under Markovian Regime-switching Model
Elliott, Robert; Siu, T, Methodology and Computing in Applied Probability 11 (145–157) 2008 |
General tooth boundary conditions for equation free modeling
Roberts, Anthony John; Kevrekidis, I, Siam Journal on Scientific Computing 29 (1495–1510) 2007 |
Statistical characteristics of rainstorms derived from weather radar images
Qin, J; Leonard, Michael; Kuczera, George; Thyer, M; Lambert, Martin; Metcalfe, Andrew, 30th Hydrology and Water Resources Symposium, Launceston, Tasmania 04/12/06 |
Diversity sensitivity and multimodal Bayesian statistical analysis by relative entropy
Leipnik, R; Pearce, Charles, The ANZIAM Journal 47 (277–287) 2005 |
Impinging laminar jets at moderate Reynolds numbers and separation distances
Bergthorson, J; Sone, K; Mattner, Trent; Dimotakis, P; Goodwin, D; Meiron, D, Physical Review E. (Statistical, Nonlinear, and Soft Matter Physics) 72 (066307-1–066307-12) 2005 |
Class-of-service mapping for QoS: A statistical signature-based approach to IP traffic classification
Roughan, Matthew; Sen, S; Spatscheck, O; Duffield, N, ACM SIG COMM 2004, Taormina, Sicily, Italy 25/10/04 |
Swift-Hohenberg model for magnetoconvection
Cox, Stephen; Matthews, P; Pollicott, S, Physical Review E. (Statistical, Nonlinear, and Soft Matter Physics) 69 (066314-1–066314-14) 2004 |
The Oxford dictionary of statistical terms
Dodge, Y; Cox, D; Commenges, D; Solomon, Patricia; Wilson, S, |
Higher-order statistical moments of wave-induced response of offshore structures via efficient sampling techniques
Najafian, G; Burrows, R; Tickell, R; Metcalfe, Andrew, International Offshore and Polar Engineering Conference 3 (465–470) 2002 |
Statistical modelling and prediction associated with the HIV/AIDS epidemic
Solomon, Patricia; Wilson, Susan, The Mathematical Scientist 26 (87–102) 2001 |
Statistical analysis of medical data: New developments - Book review
Solomon, Patricia, Biometrics 57 (327–328) 2001 |
Meta-analysis, overviews and publication bias
Solomon, Patricia; Hutton, Jonathon, Statistical Methods in Medical Research 10 (245–250) 2001 |
A GUI for computing flows past general airfoils
Simakov, Sergey; Dostovalova, Anna; Tuck, Ernest, The MATLAB User Conference 2000, Melbourne, Australia 09/11/00 |
Disease surveillance and data collection issues in epidemic modelling
Solomon, Patricia; Isham, V, Statistical Methods in Medical Research 9 (259–277) 2000 |
Disease surveillance and intervention studies in developing countries
Solomon, Patricia, Statistical Methods in Medical Research 9 (183–184) 2000 |
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