We are a multidisciplinary group of researchers who develop new data science methods and deploy data analytics techniques for measuring and improving the productivity, efficiency and performance of organisations.
PEMA offers professional development workshops covering theory and applications for the latest advances in data science and data analytics methods. The hands-on workshops are delivered by world recognised experts and are tailored at the executive researcher level.
The PEMA workshop platform is modelled on the renowned workshops offered by the MEAFA research group for more than a decade.Ìý
The workshops are widely recognised by academia, private sector, state and federal governmental agencies, and non-profit organisations.
The net proceeds from PEMA's professional development workshops go to funding PEMA PhD scholarships and Research development programs.
°Õ¾±³Ù±ô±ð:ÌýMachine Learning on Text Documents
¶Ù²¹³Ù±ð²õ:Ìý2-6 December 2019
Presenters:ÌýAlexander Semenov, Social Media Analysis Group, Faculty of Information Technology, the University of Jyvaskyla, Finland. Alexander Veremyev, Department of Industrial Engineering & Management Systems, University of Central Florida, USA
¶Ù±ð²õ³¦°ù¾±±è³Ù¾±´Ç²Ô:ÌýText analysis involves the process of retrieving, managing, structuring and analysing unstructured text, deriving patterns from structured data using statistical and machine learning algorithms, for evaluation and inference. In this workshop, the focus is the analysis of text documents using machine learning methods. Typical applications include finding/extracting relevant information from the text, text categorisation, document summarization, text clustering, sentiment analysis, personality analysis, concept extraction, analysis of semantic relations, and more.
PEMA's professional development workshops are widely recognised for their high quality state-of-the-art content, as evidenced by our participant organisations.
PEMA organises research workshops, research meetings, and research seminars from world experts on data science and data analytics on measuring and analysing organizational productivity, efficiency and performance. PEMA has inherited the rich experience in workshops and events from the MEAFA research group.
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Title: Ìý
Date:ÌýFriday, 31 January 2025 10:00 am AEST
Description: The first part of this talk introduces the concepts of power and sample size calculations such as alpha levels, test power, and minimum detectable effect size. First, we show Ìýhow to make several manual simple calculations and how to replicate these calculations using Stata's -power- suite of commands. Then, we show how to create tables and graphs for power, sample size, and minimum detectable effect sizes for a range of possible values, followed by a discussion of strategies for increasing statistical power. The second part of this talk demonstrates how to calculate power using simulation-based methods and show how to create your own custom power calculation programs that leverage Stata's -power- command to create custom tables and graphs. Examples will include the power simulation of simple tests, regression coefficients, and interactions term from a multilevel model. Along the way you will learn how to create simulated datasets, use Stata's -simulate- command, and how to write your own Stata commands using -program- and -syntax-.
Title:ÌýÌý
Date:ÌýFriday, 3 May 2024 11:00 am AEST
Description: The paper proposes a regularized mode estimator of unit inefficiency in a panel data context, allowing inefficiencies to vary across units and over time. This regularized estimator penalizes the likelihood function by constraining the sample average of the idiosyncratic error to zero. Extensive simulations demonstrate that the regularized conditional mode estimator outperforms existing estimators, such as the unregularized mode estimator and the conditional mean estimator, particularly for the least efficient firms.
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Date: Apr 19, 2024 11:00 am AEST
Description: The paper proposes an AI approach to explore the universe of M&A opportunities and automate the M&A decision-making process. Tracing previous M&As and mining high-dimensional financial data, the AI model can accurately identify potential acquirers and targets in advance and project market reaction and synergy of M&As.
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Title:Ìý
Date:ÌýApr 5, 2024 11:00 am AEST
Description:ÌýThe statistical framework for the Malmquist productivity index (MPI) is now well developed and emphasizes the importance of developing such a framework for its alternatives. We try to fill this gap in the literature for another popular measure, known as Hicks–Moorsteen Productivity Index (HMPI).
Title:ÌýAustralian Insured Lives industry engagement workshop
Date: 7 September 2023
Description: The industry engagement workshop will brief executives of the life insurance industry industry on scope and significance of proposed Australian Insured Lives initiative. AÌýpilot projectÌýis already under way involving the PEMA research group. The workshop is open only for life insurance industry representatives. If you would like to participate as a representative of your organisation, then please get in touch atÌýbusiness.pema@sydney.edu.au.
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Title:Ìý
Date:Ìý29 May 2023
Description: This workshop outlined methods and applications of machine learning in business was co-hosted with theÌýTime Series and Forecasting research groupÌýand theÌýBusiness Finance and Banking research group. The workshop included research presentations and a discussion panel with industry experts.
°Õ¾±³Ù±ô±ð:ÌýStataCorp lecture on Extended Regression Models (ERMs)
Date:Ìý19 August 2019
Presenter:ÌýChuck Huber, Associate Director of Statistical Outreach at StataCorp
Description:ÌýThis lecture briefly reviewed the types of complications that ERMs can address and showed how to make inferences despite those complications.
Amsler, C., James, R., Prokhorov, A., Schmidt, P. (2023). Improving Predictions of Technical Inefficiency. Advances in Econometrics, in press.
Tran, K., Tsionas, M., Prokhorov, A. (2023). Semiparametric Estimation of Spatial Autoregressive Smooth-Coefficient Panel Stochastic Frontier Models. European Journal of Operational Research, 304(3), 1189-1199.
Campbell, D., Grant, A., & Thorp, S. (2022). Reducing credit card delinquency using repayment reminders. Journal of Banking and Finance, 142, 106549
Mamonov, M., Parmeter, C., Prokhorov, A. (2022). Dependence Modeling in Stochastic Frontier Analysis. Dependence Modeling, 10(1), 123-144.
Christodoulou, D., Samuell, D., Slonim, R., Tausch, F. (2022). Counteracting dishonesty strategies: A field experiment in life insurance underwriting. Journal of Behavioral Decision Making, in press.
Zhai, T., James, R., Prokhorov, A. (2022). Technical and allocative inefficiency in production systems: a vine copula approach. Dependence Modeling, 10(1), 145-158.
Merkert, R. (2022). The impact of engine standardization on the cost efficiency of airlines. Research in Transportation Business & Management, Published online: 25 February 2022, 100797.
Merkert, R., Hakim, M. (2022). Travel agency transaction costs in airline value chains - A risk in distribution channels in South Asia? Annals of Tourism Research, 95, 103414.Ìý
Grant, A., Johnstone, D., & Kwon, O. K. (2021). A cumulative prospect theory explanation of gamblers cashing-out. Journal of Mathematical Psychology, 102, 102534
Amsler, C., Prokhorov, A., Schmidt, P. (2021). A new family of copulas, with application to estimation of a production frontier system. Journal of Productivity Analysis, 55(1), 1-14.
Prokhorov, A., Tran, K., Tsionas, M. (2021). Estimation of semi- and nonparametric stochastic frontier models with endogenous regressors. Empirical Economics, 60(6), 3043-3068.
Christodoulou, D., Samuell, D. (2020). The adviser effect on insurance disclosures. Applied Economics, 52(5), 519-527.Ìý
Peng, Z., Johnstone, D., Christodoulou, D. (2020). Asymmetric impact of earnings news on investor uncertainty. Journal of Business Finance and Accounting, 47(1-2), 3-26. [
Merkert, R., Bushell, J. (2020). Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control. Journal of Air Transport Management, 89, 101929.Ìý
Grant, A., & Deer, L. (2020). Consumer marketplace lending in Australia: Credit scores and loan funding success. Australian Journal of Management, 45, 607–623.
Prokhorov, A. (2024).ÌýEfficiency and Productivity Analysis: Using Copulas in Stochastic Frontier Models. United Kingdom: Routledge.Ìý
Merkert, R., Bushell, J. (2021). The Future of Air Transport. In R. Vickerman (Eds.), International Encyclopedia of Transportation, (pp. 203-207). New York, USA: Elsevier.
Satchell, S., & Grant, A. (Eds.). (2021). Market momentum: Theory and practice. Chichester, UK: Wiley.
Merkert, R. (2020). Air transport in regional, rural and remote areas. In L. Budd, S. Ison (Eds.), Air Transport Management: An International Perspective (2nd ed.), (pp. 357-372). Abingdon: Routledge.Ìý
Christodoulou, D., Samuell, D. (2020). Do advisors skew disclosures? - Journal of the Australian and New Zealand Institute of Insurance and Finance (ANZIIF).
