ORCID Profile
0000-0002-7465-9356
Current Organisation
University of Queensland
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Statistical data science | Optimisation | Computational statistics | Statistics
Publisher: Elsevier BV
Date: 02-2021
Publisher: SAGE Publications
Date: 08-08-2022
DOI: 10.1177/00491241221113877
Abstract: Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for the optimum design, we propose a new two-stage algorithm, the Fisher Information Maximizer (FIM), which utilizes estimates from generalized linear models to find a global optimal grouping scheme among all possible [Formula: see text]-group schemes. After we define, decompose, and calculate different types of regressor-specific design errors, our analyses from both simulation and empirical ex les suggest that: 1) the optimum design of GRC counts is able to reduce the grouping error to zero, 2) the performance of modified Poisson estimators using GRC counts can be comparable to that of Poisson regression, and 3) the optimum design is usually able to achieve the same estimation efficiency with a smaller s le size.
Publisher: World Scientific Pub Co Pte Lt
Date: 18-04-2017
DOI: 10.1142/S0219530517500026
Abstract: Spectral algorithms form a general framework that unifies many regularization schemes in learning theory. In this paper, we propose and analyze a class of thresholded spectral algorithms that are designed based on empirical features. Soft thresholding is adopted to achieve sparse approximations. Our analysis shows that without sparsity assumption of the regression function, the output functions of thresholded spectral algorithms are represented by empirical features with satisfactory sparsity, and the convergence rates are comparable to those of the classical spectral algorithms in the literature.
Publisher: Elsevier BV
Date: 06-2022
Publisher: Informa UK Limited
Date: 29-04-2022
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2021
DOI: 10.3934/MFC.2021001
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2020
DOI: 10.3934/MFC.2020010
Publisher: Informa UK Limited
Date: 13-09-2018
Publisher: Elsevier BV
Date: 04-2014
DOI: 10.1016/J.JIM.2014.02.007
Abstract: Antigenic peptides presented to T cells by MHC molecules are essential for T or B cells to proliferate and eventually differentiate into effector cells or memory cells. MHC binding prediction is an active research area. Reliable predictors are demanded to identify potential vaccine candidates. The recent kernel-based algorithm KernelRLSpan (Shen et al., 2013) shows promising power on MHC II binding prediction. Here, KernelRLSpan is modified and applied to MHC I binding prediction, which we refer to as KernelRLSpanI. Besides this, we develop a novel consensus method to predict naturally processed peptides through integrating KernelRLSpanI with two state-of-the-art predictors NetMHCpan and NetMHC. The consensus method achieved top performance in the Machine Learning in Immunology (MLI) 2012 Competition,(3) group 2. We also introduce our progress of improving our MHC II binding prediction method KernelRLSpan by diffusion map.
Publisher: Oxford University Press (OUP)
Date: 16-03-2012
DOI: 10.1093/BRAIN/AWS048
Abstract: Patients with traumatic brain injury show clear impairments in behavioural flexibility and inhibition that often persist beyond the time of injury, affecting independent living and psychosocial functioning. Functional magnetic resonance imaging studies have shown that patients with traumatic brain injury typically show increased and more broadly dispersed frontal and parietal activity during performance of cognitive control tasks. We constructed binary and weighted functional networks and calculated their topological properties using a graph theoretical approach. Twenty-three adults with traumatic brain injury and 26 age-matched controls were instructed to switch between coordination modes while making spatially and temporally coupled circular motions with joysticks during event-related functional magnetic resonance imaging. Results demonstrated that switching performance was significantly lower in patients with traumatic brain injury compared with control subjects. Furthermore, although brain networks of both groups exhibited economical small-world topology, altered functional connectivity was demonstrated in patients with traumatic brain injury. In particular, compared with controls, patients with traumatic brain injury showed increased connectivity degree and strength, and higher values of local efficiency, suggesting adaptive mechanisms in this group. Finally, the degree of increased connectivity was significantly correlated with poorer switching task performance and more severe brain injury. We conclude that analysing the functional brain network connectivity provides new insights into understanding cognitive control changes following brain injury.
Publisher: Springer Science and Business Media LLC
Date: 19-10-2016
Publisher: Wiley
Date: 03-11-2021
DOI: 10.1002/PSP.2532
Abstract: Methodological advances in demographic research, especially age‐period‐cohort (APC) analysis, primarily focus on developing new models yet often fail to consider practical concerns in empirical analysis. We propose a mixed approach that integrates multiple data imputation and structural change analysis in time series so that scholars can (i) construct pseudo age groups based on more coarsely grouped age data and (ii) identify temporal anomalies. This approach is illustrated using multiple waves of Canadian Population Census data (1981–2016). We construct pseudo age groups based on more coarse age information available in the Census data and identify a local anomaly in the temporal trajectory of homeownership in Canada's less populous provinces and territories. These findings are assessed and validated in comparison with results from more populous Canadian provinces. This research broadens the methodological repertoire for demographers, geographers, and social scientists in general and extends the literature on homeownership in an understudied area.
Publisher: Oxford University Press (OUP)
Date: 08-04-2021
DOI: 10.1111/RSSA.12678
Abstract: Grouped and right-censored (GRC) counts are widely used in criminology, demography, epidemiology, marketing, sociology, psychology and other related disciplines to study behavioural and event frequencies, especially when sensitive research topics or in iduals with possibly lower cognitive capacities are at stake. Yet, the co-existence of grouping and right-censoring poses major difficulties in regression analysis. To implement generalised linear regression of GRC counts, we derive modified Poisson estimators and their asymptotic properties, develop a hybrid line search algorithm for parameter inference, demonstrate the finite-s le performance of these estimators via simulation, and evaluate its empirical applicability based on survey data of drug use in America. This method has a clear methodological advantage over the ordered logistic model for analysing GRC counts.
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2019
DOI: 10.1142/S0219530519400037
Abstract: Nowadays, the extensive collection and analyzing of data is stimulating widespread privacy concerns, and therefore is increasing tensions between the potential sources of data and researchers. A privacy-friendly learning framework can help to ease the tensions, and to free up more data for research. We propose a new algorithm, LESS (Learning with Empirical feature-based Summary statistics from Semi-supervised data), which uses only summary statistics instead of raw data for regression learning. The selection of empirical features serves as a trade-off between prediction precision and the protection of privacy. We show that LESS achieves the minimax optimal rate of convergence in terms of the size of the labeled s le. LESS extends naturally to the applications where data are separately held by different sources. Compared with the existing literature on distributed learning, LESS removes the restriction of minimum s le size on single data sources.
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2020
DOI: 10.3934/CPAA.2020187
Publisher: Elsevier BV
Date: 11-2010
Publisher: SAGE Publications
Date: 30-01-2018
Abstract: Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses from a (semisupervised) machine-learning perspective. This article uses Poisson multinomial mixture models to conceptualize the data-generating process of count responses with grouping and right censoring and demonstrates the link between grouping-scheme choices and asymptotic distributions of the Poisson mixture. To search for the optimal grouping scheme maximizing objective functions of the Fisher information (matrix), an innovative three-step M algorithm is then proposed to process infinitely many grouping schemes based on Bayesian A-, D-, and E-optimalities. A new R package is developed to implement this algorithm and evaluate grouping schemes of count responses. Results show that an optimal grouping scheme not only leads to a more efficient s ling design but also outperforms a nonoptimal one even if the latter has more groups.
Publisher: Springer Science and Business Media LLC
Date: 17-09-2014
Publisher: University of Chicago Press
Date: 11-2016
DOI: 10.1086/689853
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 04-2010
Publisher: Elsevier BV
Date: 11-2023
Publisher: IEEE
Date: 08-0012
Publisher: Oxford University Press (OUP)
Date: 16-12-2020
DOI: 10.1093/AJE/KWAA269
Abstract: To investigate temporal patterns, sociodemographic gradients, and structural breaks in adolescent marijuana use in the United States from 1991 to 2018, we used hierarchical age-period-cohort logistic regression models to distinguish temporal effects of marijuana use among 8th, 10th, and 12th graders from 28 waves of the Monitoring the Future survey (1991–2018). Structural breaks in period effects were further detected via a dynamic-programing–based method. Net of other effects, we found a clear age-related increase in the probability of marijuana use (10.46%, 23.17%, and 31.19% for 8th, 10th, and 12th graders, respectively). Period effects showed a substantial increase over time (from 16.23% in 2006 to 26.38% in 2018), while cohort effects remained stable throughout the study period. Risk of adolescent marijuana use varied by sex, racial group, family status, and parental education. Significant structural breaks during 1995–1996, 2006–2008, and 2011–2013 were identified in different subpopulations. A steady increase in marijuana use among adolescents during the latter years of this time period was identified. Adolescents who were male, were non-Black, lived in nonintact families, and had less educated parents were especially at risk of marijuana usage. Trends in adolescent marijuana use changed significantly during times of economic crisis.
Publisher: Elsevier BV
Date: 05-2012
Publisher: Elsevier BV
Date: 11-2022
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2020
DOI: 10.3934/MFC.2020026
Start Date: 2017
End Date: 2019
Funder: Research Grants Council
View Funded ActivityStart Date: 2018
End Date: 2020
Funder: Research Grants Council
View Funded ActivityStart Date: 2019
End Date: 2021
Funder: Research Grants Council, University Grants Committee
View Funded ActivityStart Date: 2015
End Date: 2019
Funder: Research Grants Council
View Funded ActivityStart Date: 07-2023
End Date: 07-2026
Amount: $360,000.00
Funder: Australian Research Council
View Funded Activity