ORCID Profile
0000-0002-3733-1474
Current Organisation
University of New South Wales
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Econometric And Statistical Methods | Statistics | Econometric and Statistical Methods | Econometrics | Stochastic Analysis And Modelling | Statistical Theory | Simulation And Modelling | Finance | Applied Statistics | Statistical Theory | Banking, Finance and Investment | Psychological Methodology, Design and Analysis | Time-Series Analysis | Artificial Intelligence and Image Processing | Financial Econometrics | Knowledge Representation and Machine Learning | Signal Processing | Operations Research | Panel Data Analysis | Time-Series Analysis | Statistical Mechanics, Physical Combinatorics and Mathematical Aspects of Condensed Matter | Stochastic Analysis and Modelling | Psychology | Natural Resource Management | Health Economics
ANZSRC Socio-Economic Objective (SEO)
Mathematical sciences | Expanding Knowledge in the Mathematical Sciences | Finance and investment services | Information processing services | Energy systems analysis | Expanding Knowledge in Psychology and Cognitive Sciences | Prevention and treatment of pollution | Environmental Management Systems | Preference, Behaviour and Welfare | Expanding Knowledge in Economics | Biological sciences | Expanding Knowledge in the Medical and Health Sciences | Economic issues not elsewhere classified | Expanding Knowledge in the Environmental Sciences | Behaviour and Health | Health Related to Ageing | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management |
Related Links
Publications
A Signal Extraction Approach to the Estimation of Treatment and Control Curves
Publisher: Informa UK Limited
Date: 12-1991
Variational Bayes With Intractable Likelihood
Publisher: Informa UK Limited
Date: 02-10-2017
Efficient generalized cross-validation for state space models
Publisher: Oxford University Press (OUP)
Date: 1987
A structured state space approach to computing the likelihood of an ARIMA process and its derivatives
Publisher: Informa UK Limited
Date: 05-1985
Bayesian estimation of an autoregressive model using Markov chain Monte Carlo
Publisher: Elsevier BV
Date: 10-1996
The nonparametric estimation of growth curves
Publisher: Elsevier BV
Date: 04-1990
Efficient Bayesian Inference for Dynamic Mixture Models
Publisher: Informa UK Limited
Date: 09-2000
A Bayesian Approach to Robust Binary Nonparametric Regression
Publisher: Informa UK Limited
Date: 03-1998
Variable Selection and Covariance Selection in Multivariate Regression Models
Publisher: Elsevier
Date: 2005
Nonparametric regression using Bayesian variable selection
Publisher: Elsevier BV
Date: 12-1996
ROBUST BAYESIAN ESTIMATION OF AUTOREGRESSIVE‐‐MOVING‐AVERAGE MODELS
Publisher: Wiley
Date: 1997
Time-evolving psychological processes over repeated decisions.
Publisher: American Psychological Association (APA)
Date: 04-2022
DOI: 10.1037/REV0000351
Abstract: Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of "regime switching," with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A geometrical derivation of the fixed interval smoothing algorithm
Publisher: Oxford University Press (OUP)
Date: 1982
Identifying relationships between cognitive processes across tasks, contexts, and time
Publisher: Springer Science and Business Media LLC
Date: 22-06-2020
Bayesian covariance matrix estimation using a mixture of decomposable graphical models
Publisher: Springer Science and Business Media LLC
Date: 11-02-2008
A general approach to heteroscedastic linear regression
Publisher: Springer Science and Business Media LLC
Date: 30-01-2007
Bayesian inference for nonlinear structural time series models
Publisher: Elsevier BV
Date: 04-2014
Convergence of the backfitting algorithm for additive models
Publisher: Cambridge University Press (CUP)
Date: 12-1994
DOI: 10.1017/S1446788700037721
Abstract: The backfitting algorithm is an iterative procedure for fitting additive models in which, at each step, one component is estimated keeping the other components fixed, the algorithm proceeding component by component and iterating until convergence. Convergence of the algorithm has been studied by Buja, Hastie, and Tibshirani (1989). We give a simple, but more general, geometric proof of the convergence of the backfitting algorithm when the additive components are estimated by penalized least squares. Our treatment covers spline smoothers and structural time series models, and we give a full discussion of the degenerate case. Our proof is based on Halperin's (1962) generalization of von Neumann's alternating projection theorem.
Bayesian Deep Net GLM and GLMM
Publisher: Informa UK Limited
Date: 16-08-2019
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Publisher: Informa UK Limited
Date: 06-2008
Speeding Up MCMC by Efficient Data Subsampling
Publisher: Informa UK Limited
Date: 16-07-2019
Bayesian mixtures of autoregressive models
Publisher: Informa UK Limited
Date: 2011
Adaptive Metropolis–Hastings sampling using reversible dependent mixture proposals
Publisher: Springer Science and Business Media LLC
Date: 18-09-2014
Filtering and smoothing algorithms for state space models
Publisher: Elsevier BV
Date: 1989
A flexible particle Markov chain Monte Carlo method
Publisher: Springer Science and Business Media LLC
Date: 14-01-2020
Fixed interval estimation in state space models when some of the data are missing or aggregated
Publisher: Oxford University Press (OUP)
Date: 1983
Efficient Estimation of Covariance Matrices using Posterior Mode Multiple Shrinkage
Publisher: Oxford University Press (OUP)
Date: 10-09-2012
Markov chain Monte Carlo in conditionally Gaussian state space models
Publisher: Oxford University Press (OUP)
Date: 09-1996
Nonparametric regression using linear combinations of basis functions
Publisher: Springer Science and Business Media LLC
Date: 2001
Bayesian subset selection and model averaging using a centered and dispersed prior for the error variance
Publisher: Wiley
Date: 06-2006
Bayesian estimation of a random effects heteroscedastic probit model
Publisher: Oxford University Press (OUP)
Date: 07-2009
Locally adaptive nonparametric binary regression
Publisher: Informa UK Limited
Date: 06-2008
Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts
Publisher: Institute of Mathematical Statistics
Date: 2012
DOI: 10.1214/12-EJS705
Fast filtering for seasonal moving average models
Publisher: Oxford University Press (OUP)
Date: 1986
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Publisher: Oxford University Press (OUP)
Date: 07-03-2015
Rejoinder
Publisher: Informa UK Limited
Date: 09-1999
Additive Nonparametric Regression With Autocorrelated Errors
Publisher: Oxford University Press (OUP)
Date: 07-1998
Abstract: A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially non-linear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterized in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of a suitable transformation of the dependent variable. All aspects of the model are estimated simultaneously by using the Markov chain Monte Carlo method. It is shown empirically that the approach proposed works well on several simulated and real ex les.
Adaptive sampling for Bayesian variable selection
Publisher: Oxford University Press (OUP)
Date: 12-2005
Bayesian variable selection and model averaging in the arbitrage pricing theory model
Publisher: Elsevier BV
Date: 12-2010
PARSIMONIOUS ESTIMATION OF THE COVARIANCE MATRIX IN MULTINOMIAL PROBIT MODELS
Publisher: Informa UK Limited
Date: 24-11-2010
Particle efficient importance sampling
Publisher: Elsevier BV
Date: 2016
Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures
Publisher: Informa UK Limited
Date: 02-10-2014
Dissecting the Random Component of Utility
Publisher: Springer Science and Business Media LLC
Date: 08-2002
Statistical Correction of a Deterministic Numerical Weather Prediction Model
Publisher: Informa UK Limited
Date: 09-2001
A copula based Bayesian approach for paid–incurred claims models for non-life insurance reserving
Publisher: Elsevier BV
Date: 11-2014
Exact likelihood of vector autoregressive-moving average process with missing or aggregated data
Publisher: Oxford University Press (OUP)
Date: 1983
Nonparametric seemingly unrelated regression
Publisher: Elsevier BV
Date: 10-2000
Variational Bayes approximation of factor stochastic volatility models
Publisher: Elsevier BV
Date: 10-2021
When is an aggregate of a time series efficiently forecast by its past?
Publisher: Elsevier BV
Date: 04-1982
Generalized smooth finite mixtures
Publisher: Elsevier BV
Date: 12-2012
Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins
Publisher: Springer Science and Business Media LLC
Date: 10-12-2019
Discussion of “Fast sparse regression and classification” by Jerome Friedman
Publisher: Elsevier BV
Date: 07-2012
Computing the likelihood and its dierivatives for a gaussian ARMA model
Publisher: Informa UK Limited
Date: 09-1985
A fast algorithm for signal extraction, influence and cross-validation in state space models
Publisher: Oxford University Press (OUP)
Date: 1989
A unified approach to nonlinearity, structural change, and outliers
Publisher: Elsevier BV
Date: 03-2007
Modelling dependence using skew t copulas: Bayesian inference and applications
Publisher: Wiley
Date: 12-10-2012
DOI: 10.1002/JAE.1215
On the rate of convergence of the innovation representation of a moving average process
Publisher: Oxford University Press (OUP)
Date: 1985
The estimation of error standard deviation in spline regression
Publisher: Informa UK Limited
Date: 12-1992
Efficient estimation of covariance selection models
Publisher: Oxford University Press (OUP)
Date: 12-2003
Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities
Publisher: Elsevier BV
Date: 12-2010
Regression Density Estimation With Variational Methods and Stochastic Approximation
Publisher: Informa UK Limited
Date: 07-2012
On particle Gibbs samplers
Publisher: Institute of Mathematical Statistics
Date: 11-2016
DOI: 10.1214/15-AIHP695
Parsimonious covariance matrix estimation for longitudinal data
Publisher: Informa UK Limited
Date: 12-2002
Signal extraction for finite nonstationary time series
Publisher: Oxford University Press (OUP)
Date: 1987
On Gibbs sampling for state space models
Publisher: Oxford University Press (OUP)
Date: 1994
A note on reparameterizing a vector autoregressive moving average model to enforce stationarity
Publisher: Informa UK Limited
Date: 06-1986
A note on Kalman filtering for the seasonal moving average model
Publisher: Oxford University Press (OUP)
Date: 1984
Spline smoothing with repeated values
Publisher: Informa UK Limited
Date: 09-1986
Dynamic Mixture of Experts Models for Online Prediction
Publisher: Informa UK Limited
Date: 02-12-2022
Modelling Conditional Densities Using Finite Smooth Mixtures
Publisher: Wiley
Date: 15-04-2011
Note concerning the Akaike and Hannan estimation procedures for an autoregressive-moving average process
Publisher: Oxford University Press (OUP)
Date: 1977
Efficient Bayesian inference for Gaussian copula regression models
Publisher: Oxford University Press (OUP)
Date: 09-2006
Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives
Publisher: Oxford University Press (OUP)
Date: 07-01-2020
DOI: 10.1111/RSSC.12393
Abstract: The paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on s ling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo s ling. We also propose a novel antithetic variable method to generate s les from the posterior distribution of the random effects and regression coefficients, resulting in significant gains in efficiency. We apply the methodology by analysing stated preference data obtained from Australian general practitioners evaluating alternative contraceptive products. Our analysis suggests that the joint probability of discussing combinations of contraceptive products with a patient shows medical practice variation among the general practitioners, which indicates some resistance even to discuss these products, let alone to recommend them.
Subsampling sequential Monte Carlo for static Bayesian models
Publisher: Springer Science and Business Media LLC
Date: 09-09-2020
Bayesian Inference for Time Series State Space Models
Publisher: Oxford University Press
Date: 29-09-2011
DOI: 10.1093/OXFORDHB/9780199559084.013.0004
Abstract: This article provides a description of time series methods that emphasize modern macroeconomics and finance. It discusses a variety of posterior simulation algorithms and illustrates their use in a range of models. This article introduces the state space framework and explains the main ideas behind filtering, smoothing, and likelihood computation. It also mentions the particle filter as a general approach for estimating state space models and gives a brief discussion of its methods. The particle filter is a very useful tool in the Bayesian analysis of the kinds of complicated nonlinear state space models that are increasingly being used in macroeconomics. It also deals with conditionally Gaussian state space models and non-Gaussian state space models. A discussion of the advantages and disadvantages of each algorithm is provided in this article. This aims to help with the use of these methods in empirical work.
Robustly estimating the marginal likelihood for cognitive models via importance sampling
Publisher: Springer Science and Business Media LLC
Date: 10-2020
Fast Evaluation of the Distribution of the Durbin-Watson and other Invariant Test Statistics in Time Series Regression
Publisher: Informa UK Limited
Date: 09-1990
Bayesian Semiparametric Regression
Publisher: Elsevier BV
Date: 09-2000
Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.
Publisher: American Psychological Association (APA)
Date: 21-04-2022
DOI: 10.1037/MET0000458
Abstract: Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-s le descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A note on obtaining the theoretical autocovariances of an ARMA process
Publisher: Informa UK Limited
Date: 10-1982
Semiparametric Bayesian Inference for Time Series with Mixed Spectra
Publisher: Oxford University Press (OUP)
Date: 1997
Abstract: A Bayesian analysis is presented of a time series which is the sum of a stationary component with a smooth spectral density and a deterministic component consisting of a linear combination of a trend and periodic terms. The periodic terms may have known or unknown frequencies. The advantage of our approach is that different features of the data—such as the regression parameters, the spectral density, unknown frequencies and missing observations—are combined in a hierarchical Bayesian framework and estimated simultaneously. A Bayesian test to detect deterministic components in the data is also constructed. By using an asymptotic approximation to the likelihood, the computation is carried out efficiently using the Markov chain Monte Carlo method in O(Mn) operations, where nis the s le size and Mis the number of iterations. We show empirically that our approach works well on real and simulated s les.
Estimating Long-term Trends in Tropospheric Ozone Levels
Publisher: Wiley
Date: 04-2002
Nonparametric spline regression with prior information
Publisher: Oxford University Press (OUP)
Date: 1993
A bayesian approach to nonparametric bivariate regression
Publisher: Informa UK Limited
Date: 12-1997
Computing p-values for the generalized Durbin-Watson and other invariant test statistics
Publisher: Elsevier BV
Date: 10-1992
Regression density estimation using smooth adaptive Gaussian mixtures
Publisher: Elsevier BV
Date: 12-2009
Constructing priors based on model size for nondecomposable Gaussian graphical models: A simulation based approach
Publisher: Elsevier BV
Date: 05-2011
On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
Publisher: Elsevier BV
Date: 12-2012
Multivariate probit models for conditional claim-types
Publisher: Elsevier BV
Date: 04-2009
New estimation approaches for the hierarchical Linear Ballistic Accumulator model
Publisher: Elsevier BV
Date: 06-2020
Locally Adaptive Semiparametric Estimation of the Mean and Variance Functions in Regression Models
Publisher: Informa UK Limited
Date: 12-2006
Accuracy and efficiency of alternative spline smoothing algorithms
Publisher: Informa UK Limited
Date: 04-1993
Subsampling MCMC - an Introduction for the Survey Statistician
Publisher: Springer Science and Business Media LLC
Date: 12-2018
Mixed Marginal Copula Modeling
Publisher: Informa UK Limited
Date: 11-07-2020
Estimation and variable selection in nonparametric heteroscedastic regression
Publisher: Springer Science and Business Media LLC
Date: 2003
Efficient estimation and prediction in time series regression models
Publisher: Oxford University Press (OUP)
Date: 1985
Adaptive Independent Metropolis–Hastings by Fast Estimation of Mixtures of Normals
Publisher: Informa UK Limited
Date: 2010
Markov chain Monte Carlo estimation of autoregressive models with application to metal pollutant concentration in sludge
Publisher: Elsevier BV
Date: 11-1995
Multivariate Stochastic Volatility Models with Correlated Errors
Publisher: Informa UK Limited
Date: 09-2006
Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data
Publisher: Informa UK Limited
Date: 09-1986
Variable Selection and Function Estimation in Additive Nonparametric Regression Using a Data-Based Prior
Publisher: Informa UK Limited
Date: 09-1999
Model selection in spline nonparametric regression
Publisher: Oxford University Press (OUP)
Date: 2002
Abstract: A Bayesian approach is presented for model selection in nonparametric regression with Gaussian errors and in binary nonparametric regression. A smoothness prior is assumed for each component of the model and the posterior probabilities of the candidate models are approximated using the Bayesian information criterion. We study the model selection method by simulation and show that it has excellent frequentist properties and gives improved estimates of the regression surface. All the computations are carried out efficiently using the Gibbs s ler.
A Bayesian approach to additive semiparametric regression
Publisher: Elsevier BV
Date: 10-1996
Nonparametric spline regression with autoregressive moving average errors
Publisher: Oxford University Press (OUP)
Date: 1992
Testing for linearity in a semiparametric regression model
Publisher: Elsevier BV
Date: 09-1994
Parallel Variational Bayes for Large Datasets With an Application to Generalized Linear Mixed Models
Publisher: Informa UK Limited
Date: 02-04-2016
Flexible and Robust Particle Tempering for State Space Models
Publisher: Elsevier BV
Date: 10-2022
Diagnostics for Time Series Analysis
Publisher: Wiley
Date: 05-1999
Nonparametric estimation of the distribution function in contingent valuation models
Publisher: Institute of Mathematical Statistics
Date: 09-2009
DOI: 10.1214/09-BA421
Local and global identification and strong consistency in time series models
Publisher: Elsevier BV
Date: 12-1978
Bayesian variable selection and model averaging in high-dimensional multinomial nonparametric regression
Publisher: Informa UK Limited
Date: 03-2003
Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models
Publisher: Informa UK Limited
Date: 2008
A NOTE ON SQUARE ROOT FILTERING FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS
Publisher: Wiley
Date: 05-1990
Speeding up MCMC by Delayed Acceptance and Data Subsampling
Publisher: Informa UK Limited
Date: 28-07-2018
Bayesian inference using adaptive sampling
Publisher: Elsevier
Date: 2008
Flexible Multivariate Density Estimation With Marginal Adaptation
Publisher: Informa UK Limited
Date: 10-2013
A note on an alternative derivation of the likelihood of an autoregressive moving average process
Publisher: Elsevier BV
Date: 1981
A duality formula for Feynman–Kac path particle models
Publisher: Elsevier BV
Date: 05-2015
A Bayesian approach to model selection in stochastic coefficient regression models and structural time series models
Publisher: Elsevier BV
Date: 1997
The Performance of Cross-Validation and Maximum Likelihood Estimators of Spline Smoothing Parameters
Publisher: Informa UK Limited
Date: 12-1991
A BAYESIAN APPROACH TO ESTIMATING AND FORECASTING ADDITIVE NONPARAMETRIC AUTOREGRESSIVE MODELS
Publisher: Wiley
Date: 03-1996
Prediction mean squared error for state space models with estimated parameters
Publisher: Oxford University Press (OUP)
Date: 1986
Asymptotic Estimation and Hypothesis Testing Results for Vector Linear Time Series Models
Publisher: JSTOR
Date: 07-1979
DOI: 10.2307/1914144
FILTERING AND SMOOTHING IN STATE SPACE MODELS WITH PARTIALLY DIFFUSE INITIAL CONDITIONS
Publisher: Wiley
Date: 07-1990
Related Organisations
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View Funded ActivitySpecial Research Initiatives - Grant ID: SR0354895
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View Funded ActivityARC Centres Of Excellence - Grant ID: CE140100049
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