Previous talks 2014/2015
Wednesday 5th May 2015:Random walking on mysterious light beams across night sky: An interesting half strip model with applications
Speaker: Chak Hei Lo, Durham University
Abstract
Many random processes arising in applications exhibit a range of possible behaviours depending upon the values of certain key factors. Investigating critical behaviour for such systems leads to interesting and challenging mathematics. Much progress has been made over the years, using various techniques. The most subtle case is when the system is near a critical point. This presentation will give a brief introduction to a near-critical random walk model, demonstrating various applications in economics, physics and logistics.
Wednesday 22nd April 2015:The Role of Self-discipline in Predicting Achievement for 10th Graders
Speaker: Rui Zhao, Durham University
Abstract
This study investigated how sub-dimensions of self-discipline (behavioral control, thinking control, and emotional control) in predicting 10th graders` achievement. A total of 608 10th graders were recruited in tshis study. Self-discipline was measured by The Middle School Students’ Self-control Ability Questionnaire. Students’ previous academic achievement is assessed by the Senior High School Entrance Examination (SHSEE, known as “Zhongkao”), and the composite scores of a school monthly exam served as the later achievement. Results show a certain amount of mediating effect that behavioral, thinking, and emotional control have in predicting academic achievement. Those sub-dimensions add small, but incremental variance to explain later academic achievement.
Wednesday 11th March 2015:Modelling the competition: from fighting lizards to journal citations
Speaker: Dr. Helen Ogden, University of Warwick
Abstract
I will talk about models for tournament data. Some applications of these models are obvious -- for example, to rank sports players based on the outcomes of matches played between them. But competition models can also be used in some slightly surprising areas. I will discuss examples from animal behaviour (modelling fights between lizards) and bibliometrics (ranking journals, based on the citations between them). Competition models also provide some interesting statistical challenges, and I will briefly discuss my own work on improving the approximations which are used for inference in some of these models.
Wednesday 25th February 2015:Fisher information under Gaussian quadrature models
Speaker: Hermes Marques Da Silva Junior, University of Durham
Abstract
We develop explicit expressions to compute the Fisher information matrix for the estimation of random effect models through Gaussian Quadrature. Illustrative examples using real data application and simulated data are provided.
Wednesday 11th February 2015:Gender Discrimination in Academia: Afghan Context
Speaker: Y. Afzali, University of Durham
Abstract
In this seminar I am going to discuss the findings of a survey of perceptions of gender discrimination in academia in Afghanistan. The aim is to explore how female and male academics perceive the level of overt discrimination in various aspects of academic life in an Afghan context. SPSS (Statistical Package for Social Sciences) is used to analyse the quantitative data. Bivariate crosstabulation and chi-square tests of statistical significance are used to explore possible differences between male and female academics with respect to their perceptions of gender (in)equality in their workplace. Multivariate crosstabulation and binary logistic regression is also used to explore how respondents’ perceptions of discrimination are shaped by the interaction of gender with their other characteristics, both personal and professional.
Wednesday 28th January 2015:Patterns and Processes Revealed in High-Frequency Environmental Data
Speaker: A. Elayouty, University of Glasgow
Abstract
Advances in sensor technology enable monitoring programmes to record and store measurements at a high temporal resolution, enhancing the capacity to detect and understand short duration changes that would not have been apparent in the past with monthly, fortnightly or even daily sampling. Although these high-frequency data are advantageous, there are challenges in their processing and analysis such as the large volumes of data, their complex behaviour over the different timescales and the strong correlation structure that persists over a large number of lags. The aim of this talk is to present the complexities of modelling high-frequency data which arise from environmental applications. Surface waters are considered as key sources of atmospheric CO2, thus comprehensive understanding of the CO2 dynamics in surface waters is valuable. We consider a 15-minute resolution sensor-generated time series of the over-saturation of CO2, EpCO2, in a small order river system of the River Dee. Advanced statistical approaches used to analyse and model the data, which include visualization tools for exploratory analysis, wavelets, generalized additive models and functional data analysis, will be presented. These methods reveal the complex dynamics of EpCO2 over different timescales, the multivariate relationships of EpCO2 with hydrology and the temporal auto-correlation structures, which are time and scale dependent.
Wednesday 21st January 2015:Hamiltonian Monte Carlo and its variants
Speaker: D. Tang, Durham University
Abstract
Hamiltonian Monte Carlo (HMC), also known as Hybrid Monte Carlo, is one of the Markov Chain Monte Carlo (MCMC) sampling methods which offer different strategies to generate a sequence of correlated samples converging to the desired distribution. In many situations, especially Bayesian statistics, target distributions usually have complicated forms, high correlated parameters and large dimension size. Traditional MCMC methods, such as random-walk Metropolis Hasting and Gibbs sampling, might have slow exploration of state space and low accepted rate caused by both random walk behaviour of traditional MCMC methods and the complex nature of target distributions. HMC is a new sampling algorithm which tries to avoid these problems by taking several steps according to gradient information of target distribution. This makes HMC have remote proposals and converge quicker than traditional random walk methods. Although the demonstrated ability of HMC sampling to overcome random walks in MCMC sampling suggests that it should be a highly successful tool for Bayesian inference, its performances depend on its algorithm parameters. Three HMC variants that provides automatically tuning will be discussed in the talk.
Wednesday 10th December 2014:TBA
Speaker: E. Waldmann, University of Liverpool
Abstract
Under many circumstances the application of classical (generalized) linear regression is not enough to describe the relationship between a set of covariates and a dependent variable. Especially the key assumption of a closed form distribution is violated frequently. One of the approaches to overcome those problems is quantile regression, developed by Roger Koenker in the 1970s. Even though quantile regression is widely used by now,there is no standard approach for modelling the impact of covariates on two or more dependent variables simultaneously. Our developments are motivated by the analysis of data from the field of biodiversity, where we want to use covariates, like temperature, topographic diversity (the maximal elevational range within one region), habitatial diversity (the abundance of different ecosystems in one region) and the number of rainy days to explain both, the number of animal species and plant species in one region.
Wednesday 26th November 2014:Robust Crop Rotation Modelling
Speaker: L. Paton, Mathematical Sciences
Abstract
Farmers often follow set patterns of crop choices in order to maximise profits and preserve nutrients in the soil. However, these crop choices are dependant on a variety of factors, including the climate and the economy. Modelling and predicting these crop rotations is an important task in order to analyse the effect changes in climate or the economy may have on agricultural output. One major difficulty in crop rotation modelling is the shortage of observations of some crop types. A robust Bayesian approach allows us to handle these rare crop types, by allowing us to obtain intervals of predictions which more accurately represent our knowledge. I will talk about this approach.
Wednesday 12th November 2014: Statistical shape analysis
Speaker: T.Tsiftsi, Mathematical Sciences
Abstract
The recognition of objects in images is an important problem in many branches of science. Statistics can help to solve this problem in many ways so statistical shape analysis is an integral part of object recognition. In this talk I will explain what shapes are, why they are important, how they can be used and how statistical shape analysis can help. I will try to explain why Bayesian shape analysis is so important and how supervised and unsupervised learning can help us tackle the problems. I will also give examples on how all the above can be used in geological applications.
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