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Search Results: 1 - 10 of 118 matches for " Kerrie Mengersen "
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Considering ethnicity in teaching and learning statistics: should I worry about where my students come from?
Darfiana Nur,Kerrie Mengersen
Advances in Decision Sciences , 2003, DOI: 10.1155/s1173912603000129
Abstract: In the past decade there has been strong interest in the special needs of overseas students attending Australian universities, with respect to teaching and learning. This paper reports on three action research studies that address the question of whether such issues remain in the teaching and learning of statistics in particular.
Change Point Estimation in Monitoring Survival Time
Hassan Assareh, Kerrie Mengersen
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0033630
Abstract: Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.
Detection of the Time of a Step Change in Monitoring Survival Time
Hassan Assareh,Kerrie Mengersen
Lecture Notes in Engineering and Computer Science , 2011,
Abstract:
Bayesian Estimation of the Time of a Decrease in Risk-Adjusted Survival Time Control Charts
Hassan Assareh,Kerrie Mengersen
IAENG International Journal of Applied Mathematics , 2011,
Abstract:
Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making  [PDF]
Brigitte Colin, Samuel Clifford, Paul Wu, Samuel Rathmanner, Kerrie Mengersen
Open Journal of Statistics (OJS) , 2017, DOI: 10.4236/ojs.2017.75061
Abstract: Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.
Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia
Wenbiao Hu, Kerrie Mengersen, Shilu Tong
BMC Infectious Diseases , 2010, DOI: 10.1186/1471-2334-10-311
Abstract: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models.The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.Cryptosporidiosis is a diarrhoeal disease caused by microscopic parasites of the Cryptosporidium parvum [1]. The parasite is one of the most common causes of waterborne disease in Australia and globally and is found in drinking water and recreational water [2]. Cryptosporidiosis can also be transmitted via contaminated food, contact between people, or contact between people and animals.
Identifying the Time of a Linear Trend Disturbance in Odds Ratio of Clinical Outcomes
Hassan Assareh,Ian Smith,Kerrie Mengersen
Lecture Notes in Engineering and Computer Science , 2011,
Abstract:
Bayesian Estimation of the Time of a Linear Trend in Risk-Adjusted Control Charts
Hassan Assareh,Ian Smith,Kerrie Mengersen
IAENG International Journal of Computer Science , 2011,
Abstract:
The Impact of Spatial Scales and Spatial Smoothing on the Outcome of Bayesian Spatial Model
Su Yun Kang, James McGree, Kerrie Mengersen
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0075957
Abstract: Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matérn correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.
Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity
Julyan Arbel,Kerrie Mengersen,Judith Rousseau
Statistics , 2014,
Abstract: We introduce a dependent Bayesian nonparametric model for the probabilistic modeling of membership of subgroups in a community based on partially replicated data. The focus here is on species-by-site data, i.e. community data where observations at different sites are classified in distinct species. Our aim is to study the impact of additional covariates, for instance environmental variables, on the data structure, and in particular on the community diversity. To that purpose, we introduce dependence a priori across the covariates, and show that it improves posterior inference. We use a dependent version of the Griffiths-Engen-McCloskey distribution defined via the stick-breaking construction. This distribution is obtained by transforming a Gaussian process whose covariance function controls the desired dependence. The resulting posterior distribution is sampled by Markov chain Monte Carlo. We illustrate the application of our model to a soil microbial dataset acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. This method allows for inference on a number of quantities of interest in ecotoxicology, such as diversity or effective concentrations, and is broadly applicable to the general problem of communities response to environmental variables.
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