David Gunawan

New estimation approaches for the hierarchical Linear Ballistic Accumulator model

Publisher: Elsevier BV

Date: 06-2020

DOI: 10.1016/J.JMP.2020.102368

A flexible particle Markov chain Monte Carlo method

Publisher: Springer Science and Business Media LLC

Date: 14-01-2020

DOI: 10.1007/S11222-019-09916-7

Bayesian inference for health inequality and welfare using qualitative data

Publisher: Elsevier BV

Date: 2018

DOI: 10.1016/J.ECONLET.2017.11.005

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

DOI: 10.1007/S11222-020-09969-Z

Posterior Probabilities for Lorenz and Stochastic Dominance of Australian Income Distributions*

Publisher: Wiley

Date: 25-08-2021

DOI: 10.1111/1475-4932.12628

Abstract: Using Household, Income and Labour Dynamics in Australia (HILDA) data for 2001, 2006, 2010, 2014 and 2017, we compute posterior probabilities for dominance for all pairwise comparisons of income distributions in these years. The dominance criteria considered are Lorenz dominance and first‐ and second‐order stochastic dominance. The income distributions are estimated using an infinite mixture of gamma density functions, with posterior probabilities computed as the proportion of Markov chain Monte Carlo draws that satisfy the inequalities that define the dominance criteria. We find welfare improvements from 2001 to 2006 and qualified improvements from 2006 to the later three years. Evidence of an ordering between 2010, 2014 and 2017 cannot be established.

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).

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).

Flexible and Robust Particle Tempering for State Space Models

Publisher: Elsevier BV

Date: 10-2022

DOI: 10.1016/J.ECOSTA.2022.09.003

Mixed Marginal Copula Modeling

Publisher: Informa UK Limited

Date: 11-07-2020

DOI: 10.1080/07350015.2018.1469998

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

DOI: 10.1007/S11222-018-9846-Y

Inequality in Education: A Comparison of Australian Indigenous and Nonindigenous Populations

Publisher: Walter de Gruyter GmbH

Date: 31-01-2022

DOI: 10.1515/SPP-2021-0026

Abstract: Data from the Household Income and Labour Dynamics in Australia Survey is used to estimate distributions for the level of educational attainment for Australian indigenous and nonindigenous populations for the years 2001, 2006, 2014 and 2017. Bayesian inference is used to analyse how these ordinal categorical distributions have changed over time and to compare indigenous and nonindigenous distributions. Both the level of educational attainment and inequality in educational attainment are considered. To compare changes in levels over time, as well as inequality between the two populations, first order stochastic dominance and an index of educational poverty are used. To examine changes in inequality over time, two inequality indices and generalised Lorenz dominance are considered. Results are presented in terms of posterior densities for the indices and posterior probabilities for dominance for the dominance comparisons. We find some evidence of improvement over time, especially in the lower parts of the indigenous distribution and that inequality has significantly increased from 2001 to 2017.

Wave-by-wave prediction for spread seas using a machine learning model with physical understanding

Publisher: Elsevier BV

Date: 10-2023

DOI: 10.1016/J.OCEANENG.2023.115450

Forward prediction of surface wave elevations and motions of offshore floating structures using a data-driven model

Publisher: Elsevier BV

Date: 08-2023

DOI: 10.1016/J.OCEANENG.2023.114680

Monash University

Location: Australia

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University Of Wollongong

Location: Australia

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University Of Melbourne

Location: Australia

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University Of New South Wales

Location: Australia

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Monash University

Location: No location found

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Discovery Projects - Grant ID: DP210103873

Start Date: 07-2021

End Date: 12-2024

Amount: $378,500.00

Funder: Australian Research Council

Industrial Transformation Research Hubs - Grant ID: IH200100009

Start Date: 07-2021

End Date: 06-2026

Amount: $5,000,000.00

Funder: Australian Research Council