People_

Associate Professor Garth Tarr

Director of Research Division (Statistics)
Associate Professor in Statistics and Data Science
Program Director, Master of Data Analytics

Phone

+61 2 8627 8033

Email

garth.tarr@sydney.edu.au

Address

F07 - Carslaw Building

The University of Sydney

Details

Member of Sydney Institute of Agriculture

Websites

Resume

Garth Tarr is a statistician and data scientist with expertise in feature selection in complex data and predictive modelling. He works in partnership with industry bodies and organisations to deliver actionable and impactful data driven insights. Garth is passionate about delivering exceptional learning experiences for students. As Associate Head (Education) he led the School of Mathematics and Statistics through a successful pivot to flexible delivery methods and implementation of new ways to engage and assess students at scale.

Research interests

Garth Tarr is a member of the Sydney Precision Data Science centre and the Sydney Institute of Agriculture. His research interests include robust statistics, feature selection, data visualisation, meat science, educational research as well as biostatistics and biometrics. He develops new statistical methods and software to help scientists and researchers make more informed decisions about the statistical models they build. Garth's research is driven by applications in modelling complex agricultural data and biostatistics.

Teaching and supervision

Units of study

In 2025:

SCIE4002Experimental Design and Data Analysis
ODAT5022Applied Time Series Analysis

Previous years:

DATA2002 Data Analytics: Learning from Data[2018, 2019, 2020, 2021, 2022, 2023, 2024]
DATA2902 Data Analytics: Learning from Data (Advanced) [2019, 2020, 2021, 2022, 2023, 2024]
OLET5608 Linear Modelling [2020, 2021, 2022]

PhD students

In progress

Rajan Shankar, Modern regularised approaches for prediction in complex data.
Martin Huang,Sparse modelling and regularisation: Feature selection in high-dimensional data.

Completed

Kevin Wang,A statistical framework for incorporating multi-omics technologies for precision medicine.
Holly Cuthbertson,Infra-red thermography and radio frequency identification for detection of stress in lairage.
Cassius Coombs,Developing real-time tomography for applications in the livestock industries.
Mahdi Abolghasemi, Supply chain forecasting: Predicting demand variation in the presence of promotion.
Peng Su,Regression and variable selection in the presence of cellwise outliers.

Short courses

Model selection with R

Australian National University, Canberra, Australia, 10-11 April 2017
University of Western Australia, Perth, Australia, 23-24 November 2015

Fast algorithms and modern visualisations for feature selection

International Biometrics Conference, Seoul, Korea, 5-10 July 2020
Australian Statistical Society, Sydney, Australia, 12 April 2018
International Society for Clinical Biostatistics and Australian Statistical Conference, Melbourne, Australia, 26 August 2018

Analytics for industry

Texas Tech University, Lubbock, USA, 5-9 February 2024
Meat and Livestock Australia, Armidale, Australia, 11-13 and 21-22 May 2022
Murdoch University, Perth, Australia, 14-16 June 2022
Meat and Livestock Australia, Brisbane, Australia, 8-10 and 23-24 May 2019
Meat and Livestock Australia, Brisbane, Australia, 4-6 and 19-20 April 2018
Teys Australia, Brisbane, Australia, 24-26 July and 22-23 August 2017
Meat and Livestock Australia, Brisbane, Australia, 15-19 May 2017
Australian Country Choice, Brisbane, Australia, 7-11 November 2016

Data visualisation, interactive data analysis and statistical programming. BioInfoSummer, Sydney, Australia, 11 December 2015

Model selection with R.

Australian National University, Canberra, Australia, 10-11 April 2017 (29 participants)

University of Western Australia, Perth, Australia, 23-24 November 2015 (30 participants). Fast algorithms and modern visualisations for feature selection. International Society for Clinical

Biostatistics and Australian Statistical Conference, Melbourne, Australia, 26 August 2018.

Analytics for industry.

Meat and Livestock Australia, Brisbane, Australia, 17-19 January and 1-2 February 2018. Teys Australia, Brisbane, Australia, 24-26 July and 22-23 August 2017 (11 participants). Meat and Livestock Australia, Brisbane, Australia, 15-19 May 2017 (8 participants).

Australian Country Choice, Brisbane, Australia, 7-11 November 2016 (11 participants).

Data visualisation, interactive data analysis and statistical programming. BioInfoSummer, Sydney, Australia, 11 December 2015 (90 participants).

Model selection with R.

Australian National University, Canberra, Australia, 10-11 April 2017 (29 participants)

University of Western Australia, Perth, Australia, 23-24 November 2015 (30 participants). Fast algorithms and modern visualisations for feature selection. International Society for Clinical

Biostatistics and Australian Statistical Conference, Melbourne, Australia, 26 August 2018.

Analytics for industry.

Meat and Livestock Australia, Brisbane, Australia, 17-19 January and 1-2 February 2018. Teys Australia, Brisbane, Australia, 24-26 July and 22-23 August 2017 (11 participants). Meat and Livestock Australia, Brisbane, Australia, 15-19 May 2017 (8 participants).

Australian Country Choice, Brisbane, Australia, 7-11 November 2016 (11 participants).

Data visualisation, interactive data analysis and statistical programming. BioInfoSummer, Sydney, Australia, 11 December 2015 (90 participants).

Timetable

Current projects

Are consumers willing to pay for eating quality?
Outlier identification and classification with functional data
Improved model averaging through better model weights
Finite sample performance of robust location estimators
Optimal robust location estimation in a bounded interval
Statistical issues in evaluating the eating quality of meat
Factors affecting carcase compliance in Australian feedlots
Objective carcase measurements for improved eating quality prediction

Associations

Statistical Society of Australia
Australian Mathematical Society
American Statistical Association
International Biometrics Society

Awards and honours

Vice-Chancellor’s Award for Teaching and Learning Excellence, University of Sydney, 2023.
Vice-Chancellor’s Award for Outstanding Teaching, University of Sydney. DATA1001 leadership team: Di Warren, Jean Yang, Michael Stewart, Ellis Patrick and Garth Tarr, 2021.
Vice-Chancellor’s Award for Teaching Excellence and Contribution to Student Learning, University of Newcastle. With Renner I, 2016.
EJG Pitman Prize for the most outstanding talk presented by a young statistician at the Australian Statistical Conference, 2012.
First prize for the best oral presentation by a student at the Australian Young Statisticians Conference, 2013.
Statistical Society of Australia Golden Jubilee Travel Grant, 2013.
Vice-Chancellor’s Award for Systems that Achieve Collective Excellence in Teaching and Learning, University of Sydney. With Morison K and Morr J, 2011.

International links

Netherlands

(Maastricht University) Research collaboration with Ines Wilms

Current research students

Project title	Research student
Feature Selection with Sparse Modelling and Regularisation in High-Dimensional Data	Martin HUANG
Modern regularised approaches for prediction in complex data	Rajan SHANKAR

Publications

By Type

Expand all

Book Chapters

Muir, M., Drury, H., Tarr, G., White, F. (2019). A strategy for enhancing academics' cultural lens: the Knowing Your Students report. In Jamie Hoffman, Patrick Blessinger, Mandla Makhanya (Eds.), Strategies for Facilitating Inclusive Campuses in Higher Education: International Perspectives on Equity and Inclusion, (pp. 145-162). Bingley: Emerald Publishing Limited. [More Information]

Journals

Pannier, L., Tarr, G., Pleasants, T., Ball, A., McGilchrist, P., Gardner, G., Pethick, D. (2025). The construction of a sheepmeat eating quality prediction model for Australian lamb. Meat Science, 220, 109711-1-109711-13. [More Information]
Lees, J., Hardcastle, N., Johnston, J., Wong, R., Cuthbertson, H., Tarr, G., Garmyn, A., Miller, M., Polkinghorne, R., McGilchrist, P. (2024). Australian and United States consumer acceptance of beef brisket cooked using the low and slow barbeque method. Foods, 13(19), Article 3049-13 pages. [More Information]
Su, P., Tarr, G., Mueller, S., Wang, S. (2024). CR-Lasso: Robust cellwise regularized sparse regression. Computational Statistics and Data Analysis, 197(Open Access), Article 107971 - 1-Aticle 107971 - 14. [More Information]
Abolghasemi, M., Tarr, G., Bergmeir, C. (2024). Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions. International Journal of Forecasting, 40(2), 597-615. [More Information]
Stewart, S., Toft, H., O'Reilly, R., Lauridsen, T., Esberg, J., Jorgensen, T., Tarr, G., Christensen, M. (2024). Objective grading of rib eye traits using the Q-FOM™ camera in Australian beef carcasses. Meat Science, 213, 109500-1-109500-10. [More Information]
Kinley, R., Roque, B., Mackenzie, S., Fortes, M., Palmieri, C., Tarr, G., Cuthbertson, H., Polkinghorne, R., Cowley, F. (2024). Productivity of Commercial Feedlot Beef Production Significantly Improved by Asparagopsis Bioactives Stabilized in Canola Oil. American Journal of Plant Sciences, 15(10), 899-929. [More Information]
Su, P., Tarr, G., Mueller, S. (2024). Robust variable selection under cellwise contamination. Journal of Statistical Computation and Simulation, 94(6), 1371-1387. [More Information]
Stewart, S., Gardner, G., Tarr, G. (2024). Using chemical intramuscular fat percentage to predict visual marbling scores in Australian beef carcasses. Meat Science, 217(November 2024), 109573 - 1-109573- 8. [More Information]
Coombs, C., Allman, B., Morton, E., Godoy Gimeno, M., Horadagoda, N., Tarr, G., Gonzalez, L. (2023). A preliminary investigation into the automatic detection of diseased sheep organs using hyperspectral imaging sensors. Smart Agricultural Technology, 3(February 2023), 100122-1-100122-. [More Information]
Wang, K., Pupo, G., Tembe, V., Patrick, E., Strbenac, D., Schramm, S., Thompson, J., Scolyer, R., Mueller, S., Tarr, G., Mann, G., Yang, J. (2022). Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine. npj Digital Medicine, 5(1), 85-1-85-10. [More Information]
Coombs, C., Allman, B., Morton, E., Godoy Gimeno, M., Horadagoda, N., Tarr, G., Gonzalez, L. (2022). Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors. Sensors, 22(3347), 1-16. [More Information]
Steel, C., Lees, A., Tarr, G., Dunshea, F., Bowler, D., Cowley, F., Warner, R., McGilchrist, P. (2022). Feedlot Factors Influencing the Incidence of Dark Cutting in Australian Grain-Fed Beef. Animals, 12(15). [More Information]
Tarr, G., Wilms, I. (2022). Regularized predictive models for beef eating quality of individual meals. Data Science in Science, 1(1), 20-33. [More Information]
Steel, C., Lees, A., Tarr, G., Warner, R., Warner, R., Dunshea, F., Cowley, F., McGilchrist, P. (2022). The impact of weather on the incidence of dark cutting in Australian feedlot cattle. International Journal of Biometeorology, 66(Special Issue), 263-274. [More Information]
Steel, C., Lees, A., Bowler, D., Gonzalez-Rivas, P., Tarr, G., Warner, R., Dunshea, F., Cowley, F., McGilchrist, P. (2021). Abattoir factors influencing the incidence of dark cutting in australian grain-fed beef. Animals, 11(2), 1-15. [More Information]
Pogorzelski, G., Polkinghorne, R., Tarr, G., Półtorak, A., Wierzbicka, A. (2021). Effect of "dry aging" or "wet aging" of beef on eating quality. Animal Science Papers and Reports, 39(3), 237-249.
Stewart, S., Gardner, G., McGilchrist, P., Pethick, D., Polkinghorne, R., Thompson, J., Tarr, G. (2021). Prediction of consumer palatability in beef using visual marbling scores and chemical intramuscular fat percentage. Meat Science, 181, 108322. [More Information]
Loudon, K., Tarr, G., Lean, I., McLerie, L., Leahy, N., Pethick, D., Gardner, G., McGilchrist, P. (2021). Short term magnesium supplementation to reduce dark cutting in pasture finished beef cattle. Meat Science, 180, 108560-11 pages. [More Information]
Abolghasemi, M., Beh, E., Tarr, G., Gerlach, R. (2020). Demand forecasting in supply chain: the impact of demand volatility in the presence of promotion. Computers and Industrial Engineering, 142(April 2020), 1-12. [More Information]
Cuthbertson, H., Tarr, G., Loudon, K., Lomax, S., White, P., McGreevy, P., Polkinghorne, R., Gonzalez, L. (2020). Using infrared thermography on farm of origin to predict meat quality andphysiological response in cattle (Bos Taurus) exposed to transport and marketing. Meat Science, 169, 108173-1-108173-10. [More Information]
Wang, K., Menzies, A., Silva, I., Wilmott, J., Yan, Y., Wongchenko, M., Kefford, R., Scolyer, R., Long, G., Tarr, G., Mueller, S., Yang, J. (2019). bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays. Bioinformatics, 35(8), 1350-1357. [More Information]
Wang, K., Tarr, G., Yang, J., Mueller, S. (2019). Fast and approximate exhaustive variable selection for generalised linear models with APES. Australian & New Zealand Journal of Statistics, 61(4), 445-465. [More Information]
Lees, A., Konarska, M., Tarr, G., Polkinghorne, R., McGilchrist, P. (2019). Influence of kiwifruit extract infusion on consumer sensory outcomes of striploin (M. longissimus lumborum) and outside flat (M. biceps femoris) from beef carcasses. Foods, 8(8; 332), 1-13. [More Information]
Cuthbertson, H., Tarr, G., Gonzalez, L. (2019). Methodology for data processing and analysis techniques of infrared video thermography used to measure cattle temperature in real time. Computers and Electronics in Agriculture, 167, 1-10. [More Information]
Loudon, K., Tarr, G., Lean, I., Polkinghorne, R., McGilchrist, P., Dunshea, F., Gardner, G., Pethick, D. (2019). The impact of pre-slaughter stress on beef eating quality. Animals, 9(9), 1-14. [More Information]
Loudon, K., Tarr, G., Pethick, D., Lean, I., Polkinghorne, R., Mason, M., Dunshea, F., Gardner, G., McGilchrist, P. (2019). The use of biochemical measurements to identify pre-slaughter stress in pasture finished beef cattle. Animals, 9(8), 1-27. [More Information]
Tarr, G., Mueller, S., Welsh, A. (2018). mplot: An R Package for Graphical Model Stability and Variable Selection Procedures. Journal of Statistical Software, 83(9), 1-28. [More Information]
Polkinghorne, R., Philpott, J., Perovic, J., Lau, J., Davies, L., Mudannayake, W., Watson, R., Tarr, G., Thompson, J. (2018). The effect of packaging on consumer eating quality of beef. Meat Science, 142, 59-64. [More Information]
Konarska, M., Kuchida, K., Tarr, G., Polkinghorne, R. (2017). Relationships between marbling measures across principal muscles. Meat Science, 123, 67-78. [More Information]
Tarr, G., Mueller, S., Weber, N. (2016). Robust estimation of precision matrices under cellwise contamination. Computational Statistics and Data Analysis, 93, 404-420. [More Information]
Dancer, D., Morrison, K., Tarr, G. (2015). Measuring the effects of peer learning on students' academic achievement in first-year business statistics. Studies in Higher Education, 40(10), 1808-1828. [More Information]
Tarr, G. (2015). Quantile based estimation of scale and dependence. Bulletin of the Australian Mathematical Society, 92(1), 173-175. [More Information]
Tarr, G., Weber, N., Mueller, S. (2015). The difference of symmetric quantiles under long range dependence. Statistics and Probability Letters, 98, 144-150. [More Information]
Tarr, G., Mueller, S., Weber, N. (2012). A robust scale estimator based on pairwise means. Journal of Nonparametric Statistics, 24(1), 187-199. [More Information]
Tarr, G. (2012). Small sample performance of quantile regression confidence intervals. Journal of Statistical Computation and Simulation, 82(1), 81-94. [More Information]

show 32 more

Conferences

Muir, M., Drury, H., Tarr, G., White, F., Morrison, K. (2015). A Start to Fostering Knowledge, Skills and Attitudes to Ensure Inclusive Teaching Environments. 12th annual conference of the International Society for the Scholarship of Teaching and Learning. International Society for the Scholarship of Teaching and Learning.
Muir, M., Tarr, G., Drury, H., White, F., Morrison, K. (2014). Knowing your students: An approach to encourage pedagogical creativity and change. International Society for the Scholarship of Teaching and Learning (ISSOTL), Not applicable: Not applicable.

Report

Steel, C., Lees, A., McGilchrist, P., Tarr, G., Gonzales Rivas, P., Warner, R. (2019). Evaluation of factors contributing to the incidence dark cutting in grain-fed cattle.
Polkinghorne, R., Philpott, J., Watson, R., Tarr, G. (2019). Meat Standards Australia: Mixing and Stress Trial.
Steel, C., McGilchrist, P., Gonzales Rivas, P., Warner, R., Tarr, G. (2018). Effect of weather conditions ante-mortem on the incidence of dark cutting in feedlot finished cattle - A retrospective analysis.
Polkinghorne, R., Philpott, J., Tarr, G., Watson, R., Farmer, L. (2018). Investigation of the interaction of selected value added processes on selected cuts of varied quality.
Polkinghorne, R., Campbell, M., Tarr, G. (2018). Quantifying the impact of Modified Atmospheric Packaging (MAP) and alternative packaging solutions on eating quality.
Polkinghorne, R., Philpott, J., Watson, R., Tarr, G. (2017). Impacts on consumer acceptance of beef from interactions between pH, meat colour and packaging.
Polkinghorne, R., Philpott, J., Tarr, G., Watson, R. (2017). Primal block and extended aging sensory analysis.

show 4 more

By Year

Expand all

2025

Pannier, L., Tarr, G., Pleasants, T., Ball, A., McGilchrist, P., Gardner, G., Pethick, D. (2025). The construction of a sheepmeat eating quality prediction model for Australian lamb. Meat Science, 220, 109711-1-109711-13. [More Information]

2024

Lees, J., Hardcastle, N., Johnston, J., Wong, R., Cuthbertson, H., Tarr, G., Garmyn, A., Miller, M., Polkinghorne, R., McGilchrist, P. (2024). Australian and United States consumer acceptance of beef brisket cooked using the low and slow barbeque method. Foods, 13(19), Article 3049-13 pages. [More Information]
Su, P., Tarr, G., Mueller, S., Wang, S. (2024). CR-Lasso: Robust cellwise regularized sparse regression. Computational Statistics and Data Analysis, 197(Open Access), Article 107971 - 1-Aticle 107971 - 14. [More Information]
Abolghasemi, M., Tarr, G., Bergmeir, C. (2024). Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions. International Journal of Forecasting, 40(2), 597-615. [More Information]
Stewart, S., Toft, H., O'Reilly, R., Lauridsen, T., Esberg, J., Jorgensen, T., Tarr, G., Christensen, M. (2024). Objective grading of rib eye traits using the Q-FOM™ camera in Australian beef carcasses. Meat Science, 213, 109500-1-109500-10. [More Information]
Kinley, R., Roque, B., Mackenzie, S., Fortes, M., Palmieri, C., Tarr, G., Cuthbertson, H., Polkinghorne, R., Cowley, F. (2024). Productivity of Commercial Feedlot Beef Production Significantly Improved by Asparagopsis Bioactives Stabilized in Canola Oil. American Journal of Plant Sciences, 15(10), 899-929. [More Information]
Su, P., Tarr, G., Mueller, S. (2024). Robust variable selection under cellwise contamination. Journal of Statistical Computation and Simulation, 94(6), 1371-1387. [More Information]
Stewart, S., Gardner, G., Tarr, G. (2024). Using chemical intramuscular fat percentage to predict visual marbling scores in Australian beef carcasses. Meat Science, 217(November 2024), 109573 - 1-109573- 8. [More Information]

show 4 more

2023

Coombs, C., Allman, B., Morton, E., Godoy Gimeno, M., Horadagoda, N., Tarr, G., Gonzalez, L. (2023). A preliminary investigation into the automatic detection of diseased sheep organs using hyperspectral imaging sensors. Smart Agricultural Technology, 3(February 2023), 100122-1-100122-. [More Information]

2022

Wang, K., Pupo, G., Tembe, V., Patrick, E., Strbenac, D., Schramm, S., Thompson, J., Scolyer, R., Mueller, S., Tarr, G., Mann, G., Yang, J. (2022). Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine. npj Digital Medicine, 5(1), 85-1-85-10. [More Information]
Coombs, C., Allman, B., Morton, E., Godoy Gimeno, M., Horadagoda, N., Tarr, G., Gonzalez, L. (2022). Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors. Sensors, 22(3347), 1-16. [More Information]
Steel, C., Lees, A., Tarr, G., Dunshea, F., Bowler, D., Cowley, F., Warner, R., McGilchrist, P. (2022). Feedlot Factors Influencing the Incidence of Dark Cutting in Australian Grain-Fed Beef. Animals, 12(15). [More Information]
Tarr, G., Wilms, I. (2022). Regularized predictive models for beef eating quality of individual meals. Data Science in Science, 1(1), 20-33. [More Information]
Steel, C., Lees, A., Tarr, G., Warner, R., Warner, R., Dunshea, F., Cowley, F., McGilchrist, P. (2022). The impact of weather on the incidence of dark cutting in Australian feedlot cattle. International Journal of Biometeorology, 66(Special Issue), 263-274. [More Information]

show 2 more

2021

Steel, C., Lees, A., Bowler, D., Gonzalez-Rivas, P., Tarr, G., Warner, R., Dunshea, F., Cowley, F., McGilchrist, P. (2021). Abattoir factors influencing the incidence of dark cutting in australian grain-fed beef. Animals, 11(2), 1-15. [More Information]
Pogorzelski, G., Polkinghorne, R., Tarr, G., Półtorak, A., Wierzbicka, A. (2021). Effect of "dry aging" or "wet aging" of beef on eating quality. Animal Science Papers and Reports, 39(3), 237-249.
Stewart, S., Gardner, G., McGilchrist, P., Pethick, D., Polkinghorne, R., Thompson, J., Tarr, G. (2021). Prediction of consumer palatability in beef using visual marbling scores and chemical intramuscular fat percentage. Meat Science, 181, 108322. [More Information]
Loudon, K., Tarr, G., Lean, I., McLerie, L., Leahy, N., Pethick, D., Gardner, G., McGilchrist, P. (2021). Short term magnesium supplementation to reduce dark cutting in pasture finished beef cattle. Meat Science, 180, 108560-11 pages. [More Information]

show 1 more

2020

Abolghasemi, M., Beh, E., Tarr, G., Gerlach, R. (2020). Demand forecasting in supply chain: the impact of demand volatility in the presence of promotion. Computers and Industrial Engineering, 142(April 2020), 1-12. [More Information]
Cuthbertson, H., Tarr, G., Loudon, K., Lomax, S., White, P., McGreevy, P., Polkinghorne, R., Gonzalez, L. (2020). Using infrared thermography on farm of origin to predict meat quality andphysiological response in cattle (Bos Taurus) exposed to transport and marketing. Meat Science, 169, 108173-1-108173-10. [More Information]

2019

Muir, M., Drury, H., Tarr, G., White, F. (2019). A strategy for enhancing academics' cultural lens: the Knowing Your Students report. In Jamie Hoffman, Patrick Blessinger, Mandla Makhanya (Eds.), Strategies for Facilitating Inclusive Campuses in Higher Education: International Perspectives on Equity and Inclusion, (pp. 145-162). Bingley: Emerald Publishing Limited. [More Information]
Wang, K., Menzies, A., Silva, I., Wilmott, J., Yan, Y., Wongchenko, M., Kefford, R., Scolyer, R., Long, G., Tarr, G., Mueller, S., Yang, J. (2019). bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays. Bioinformatics, 35(8), 1350-1357. [More Information]
Steel, C., Lees, A., McGilchrist, P., Tarr, G., Gonzales Rivas, P., Warner, R. (2019). Evaluation of factors contributing to the incidence dark cutting in grain-fed cattle.
Wang, K., Tarr, G., Yang, J., Mueller, S. (2019). Fast and approximate exhaustive variable selection for generalised linear models with APES. Australian & New Zealand Journal of Statistics, 61(4), 445-465. [More Information]
Lees, A., Konarska, M., Tarr, G., Polkinghorne, R., McGilchrist, P. (2019). Influence of kiwifruit extract infusion on consumer sensory outcomes of striploin (M. longissimus lumborum) and outside flat (M. biceps femoris) from beef carcasses. Foods, 8(8; 332), 1-13. [More Information]
Polkinghorne, R., Philpott, J., Watson, R., Tarr, G. (2019). Meat Standards Australia: Mixing and Stress Trial.
Cuthbertson, H., Tarr, G., Gonzalez, L. (2019). Methodology for data processing and analysis techniques of infrared video thermography used to measure cattle temperature in real time. Computers and Electronics in Agriculture, 167, 1-10. [More Information]
Loudon, K., Tarr, G., Lean, I., Polkinghorne, R., McGilchrist, P., Dunshea, F., Gardner, G., Pethick, D. (2019). The impact of pre-slaughter stress on beef eating quality. Animals, 9(9), 1-14. [More Information]
Loudon, K., Tarr, G., Pethick, D., Lean, I., Polkinghorne, R., Mason, M., Dunshea, F., Gardner, G., McGilchrist, P. (2019). The use of biochemical measurements to identify pre-slaughter stress in pasture finished beef cattle. Animals, 9(8), 1-27. [More Information]

show 6 more

2018

Steel, C., McGilchrist, P., Gonzales Rivas, P., Warner, R., Tarr, G. (2018). Effect of weather conditions ante-mortem on the incidence of dark cutting in feedlot finished cattle - A retrospective analysis.
Polkinghorne, R., Philpott, J., Tarr, G., Watson, R., Farmer, L. (2018). Investigation of the interaction of selected value added processes on selected cuts of varied quality.
Tarr, G., Mueller, S., Welsh, A. (2018). mplot: An R Package for Graphical Model Stability and Variable Selection Procedures. Journal of Statistical Software, 83(9), 1-28. [More Information]
Polkinghorne, R., Campbell, M., Tarr, G. (2018). Quantifying the impact of Modified Atmospheric Packaging (MAP) and alternative packaging solutions on eating quality.
Polkinghorne, R., Philpott, J., Perovic, J., Lau, J., Davies, L., Mudannayake, W., Watson, R., Tarr, G., Thompson, J. (2018). The effect of packaging on consumer eating quality of beef. Meat Science, 142, 59-64. [More Information]

show 2 more

2017

Polkinghorne, R., Philpott, J., Watson, R., Tarr, G. (2017). Impacts on consumer acceptance of beef from interactions between pH, meat colour and packaging.
Polkinghorne, R., Philpott, J., Tarr, G., Watson, R. (2017). Primal block and extended aging sensory analysis.
Konarska, M., Kuchida, K., Tarr, G., Polkinghorne, R. (2017). Relationships between marbling measures across principal muscles. Meat Science, 123, 67-78. [More Information]

2016

Tarr, G., Mueller, S., Weber, N. (2016). Robust estimation of precision matrices under cellwise contamination. Computational Statistics and Data Analysis, 93, 404-420. [More Information]

2015

Muir, M., Drury, H., Tarr, G., White, F., Morrison, K. (2015). A Start to Fostering Knowledge, Skills and Attitudes to Ensure Inclusive Teaching Environments. 12th annual conference of the International Society for the Scholarship of Teaching and Learning. International Society for the Scholarship of Teaching and Learning.
Dancer, D., Morrison, K., Tarr, G. (2015). Measuring the effects of peer learning on students' academic achievement in first-year business statistics. Studies in Higher Education, 40(10), 1808-1828. [More Information]
Tarr, G. (2015). Quantile based estimation of scale and dependence. Bulletin of the Australian Mathematical Society, 92(1), 173-175. [More Information]
Tarr, G., Weber, N., Mueller, S. (2015). The difference of symmetric quantiles under long range dependence. Statistics and Probability Letters, 98, 144-150. [More Information]

show 1 more

2014

Muir, M., Tarr, G., Drury, H., White, F., Morrison, K. (2014). Knowing your students: An approach to encourage pedagogical creativity and change. International Society for the Scholarship of Teaching and Learning (ISSOTL), Not applicable: Not applicable.

2012

Tarr, G., Mueller, S., Weber, N. (2012). A robust scale estimator based on pairwise means. Journal of Nonparametric Statistics, 24(1), 187-199. [More Information]
Tarr, G. (2012). Small sample performance of quantile regression confidence intervals. Journal of Statistical Computation and Simulation, 82(1), 81-94. [More Information]

Selected Grants

2022

Advanced Livestock measurement technologies, Tarr G, Meat and Livestock Australia Ltd/Research and Development Grant

2021

Fast flexible feature selection for high dimensional challenging data, Ormerod J, Tarr G, Australian Research Council (ARC)/Discovery Projects (DP)

2020

Discovery of big-data-informed multi-modal risk prediction signatures - Institute of Data Discovery for Health (D24H), Yang J, Patrick E, Yang P, Tarr G, Innovation & Technology Commission, Hong Kong SAR Govt/AIR@InnoHK
Cloud-accelerated analytics for clinical omic and imaging data - Institute of Data Discovery for Health (D24H), Yang J, Simpson S, Patrick E, Yang P, Tarr G, Kim J, Innovation & Technology Commission, Hong Kong SAR Govt/AIR@InnoHK
Evaluating of shade and shelter solutions in a southern Australian feedlot, Lees A, Tarr G, Meat and Livestock Australia Ltd/Research Development and Adoption Project

2019

Advanced measurement technologies for globally competitive Australian meat value chains, Tarr G, Meat and Livestock Australia Ltd/Research and Development Grant

2017

Advanced measurement technologies for globally competitive Australian meat value chains, Tarr G, Meat and Livestock Australia Ltd/Research and Development Grant

Software

Tarr G, Müller S and Welsh AH (2018). mplot: Graphical model stability and model selection procedures. R package. garthtarr.github.io/mplot
Tarr G (2017). StatStar: An interactive statistics education portal. statstar.io
Tarr G (2015). pairsD3: D3 scatterplot matrices. R package. github.com/garthtarr/pairsD3
Tarr G and Patrick E (2015). edgebundleR: Circle plots with bundled edges. R package. github.com/garthtarr/edgebundleR

Faculty of Science

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