School of Mathematics and Statistics - Research Publications

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    Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications
    Qian, G ; Wu, Y ; Ferrari, D ; Qiao, P ; Hollande, F (HINDAWI LTD, 2016)
    Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.
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    Setwise and Filtered Gibbs Samplers for Teletraffic Analysis
    Andrew, LLH ; Qian, G ; Vazquez-Abad, FJ (ASSOC COMPUTING MACHINERY, 2010-04)
    A setwise Gibbs sampler (SGS) method is developed to simulate stationary distributions and performance measures of network occupancy of Baskett-Chandy-Muntz-Palacios (BCMP) telecommunication models. It overcomes the simulation difficulty encountered in applying the standard Gibbs sampler to closed BCMP networks with constant occupancy constraints. We show Markov chains induced by SGS converge to the target stationary distributions. This article also investigates the filtered Gibbs sampler (FGS) as an efficient method for estimating various network performance measures. It shows that FGS's efficiency is considerable, but may be improperly overestimated. A more conservative performance estimator is then presented.
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    MODEL SELECTION AND CLAIM FREQUENCY FOR WORKERS' COMPENSATION INSURANCE
    Cui, J ; Pitt, D ; Qian, G (CAMBRIDGE UNIV PRESS, 2010-11)