Ivy Liu received a PhD in Statistics from University of Florida, Gainesville, Florida, USA.
In 2000, she joined the Statistics and Operations Research group as a lecturer.
Now, she is an associate professor in the School of Mathematics and Statistics at
Victoria University of Wellington, New Zealand.
Her main research area is in Categorical Data Analysis, including ordinal response data analysis,
cluster analysis, longitudinal data analysis, repeated measurements, and their applications.
Administration
- Deputy Director in
Centre for Data Science and Artificial Intelligence
- Postgraduate Coordinator for Statistics in
School of Mathematics and Statistics
- Head of School in
School of Mathematics and Statistics (2019 - 2022)
- Deputy Head of School in
School of Mathematics and Statistics (2014 - 2019)
- Programme Director of the Statistics and Operations Research programmes (2008 - 2013)
Research Interests
My main area of interest is categorical data analysis, particularly
- Ordinal Response Models
- Repeated Measurements
- Cluster Analysis
- Applications in a wide range of fields
Multidisciplinary Research Impact
Education and Psychometrics: Differential Item Functioning (DIF)
DIF analysis is commonly used in large-scale educational tests and survey studies. DIF analysis helps reveal whether the test or survey is fair, or whether it is systematically biased against certain groups of test-takers, such as women or certain underprivileged groups. The Liu-Agresti estimator (Liu and Agresti, 1996, DOI: 10.2307/2532838) is widely used when performance is scored according to the degrees of proficiency, rather then correct or incorrect. The Liu-Agresti method can be implemented by packages DIFAS 5.0, WINSTEPS (DIF statistics), etc.
Econometrics and Biomedical Science: Blow-Up and Cluster (BUC)
The BUC method (Mukherjee, Ahn, Liu, Rathouz, and Sanchez, 2008, DOI: 10.1002/sim.3325) is named by Baetschmann et al. (2015). For a longitudinal study, researchers are often concerned about the unobserved heterogeneity. The BUC method provides a simple way to obtain consistent fixed effect estimators for an ordered logit model to control the unobserved heterogeneity. The BUC method was originally proposed for biomedical case-control data. Gunasekara, Carter, Liu, , Richardson, and Blakely (2012) first used the method for a longitudinal data.
Social Science: Multiple response data
A multiple response variable is a categorical variable for which respondents can select any number of outcome categories. Such data arise often in a survey. For example, a question might be ``which of the following learning methods have you used'' and the respondents could tick all items that apply. We (DOI: 10.1111/j.0006-341X.1999.00936.x, DOI: 10.1177/0049124101029004001, DOI: 10.1002/bimj.200710395, DOI: 10.1111/insr.12015) used maximum-likelihood or quasi-likelihood approaches to analyse such data. Some of the methods can be implemented using packages SPSS, R (MRCV), etc.
Research Projects, Funding and Scholarships
Contact me if you want to do a PhD/MSc in my
research areas and want to get a scholarship.
Research
Assistantships:
- My research grants can support several research assistants. Succesful
applicants will be paid.
- Applicants are expected to have a B+/A- or above in STAT or MATH
papers, and have strong programming skills in R.
- If you want to gain work experience, please contact me.
Research publications
- Preedalikit, K., Fernandez, D., Liu, I., McMillan, L., Ruscone, M. N., and Costilla, R. (2023) Row mixture-based clustering with covariates for ordinal responses. Computational Statistics, https://doi.org/10.1007/s00180-023-01387-9.
- Li, S., Fan, Z., Liu, I., Morrison, P. S., and Liu, D. (2023). Surrogate method for partial association between mixed data with application to well-being survey analysis. arXiv preprint arXiv:2306.05362.
- Zeng, D., Liu, I., Bi., Y., Vennell, R., Briscoe, D., Xue, B., and Zhang, M. (2023). A new multi-object tracking pipeline based on computer vision techniques for mussel farms. Journal of the Royal Society of New Zealand, https://doi.org/10.1080/03036758.2023.2240466.
- Fernandez, D., Liu, I., Preti, A., Haro, M., and Siddi, S. (2023). Launay–Slade Hallucination Scale-Extended: simplifying its interpretation. Psychosis, 15(1), p. 56-65, DOI: 10.1080/17522439.2021.1983011.
- Liu, I., Suesse, T., Harvey, S., Gu, P. Y., Fernandez, D. and Randal, J. (2022). Generalized Mantel–Haenszel Estimators for
Simultaneous Differential Item Functioning Tests. Educational and Psychological Measurement, p.00131644221128341.
- Fernandez, D., McMillan, L., Arnold, R., Spiess, M. and Liu, I. (2022). Goodness-of-Fit and Generalized Estimating Equation Methods for
Ordinal Responses based on the Stereotype Model. Stats 5(2), 507-520. DOI: 10.3390/stats5020030.
- Fernandez, D., Gine-Vazquez, I., Liu, I. , Yucel, R., Ruscone, M. N., Morena, M., Garcia, V. G., Haro, J. M. Pan, W., Tyrovolas, S. (2021). Are environmental pollution and biodiversity levels associated to the spread and mortality of COVID-19? A four-month global analysis. Environmental Pollution, 271, 116326. DOI: 10.1016/j.envpol.2020.116326.
- Liu, I. and Fernandez, D. (2020). Discussion on “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow. Biometrics, 76(2), 564-568, DOI: 10.1111/biom.13251.
- Spiess, M., Fernandez, D., Nguyen, T., and Liu, I. (2020). Generalized estimating equations to estimate the ordered stereotype logit model for panel data. Statistics in Medicine, 39(14), 1919-1940, DOI: 10.1002/sim.8520.
- Hirose, Y. and Liu, I. (2020). Statistical Generalized Derivative Applied to the Profile Likelihood Estimation in a Mixture of Semiparametric Models. Entropy, 22(3), DOI: 10.3390/e22030278.
- Fernandez, D., Liu, I., Costilla, R., and Gu, P. Y. (2020). Assigning Scores for Ordered Categorical Responses. Journal of Applied Statistics, 47, 1261-1281, DOI: 10.1080/02664763.2019.1674790.
- Fernandez, D., Liu, I., Arnold, R., Nguyen, T. and Spiess, M. (2020). Model-based goodness-of-fit tests for the ordered stereotype model. Statistical Methods in Medical Research, 29(6), 1527-1541, DOI: 10.1177/0962280219864708.
- Fernandez, D., Liu, I., and Costilla, R. (2019). A Method for Ordinal Outcomes: The Ordered Stereotype Model. International Journal of Methods in Psychiatric Research, 28(4), DOI: 10.1002/mpr.1801.
- Costilla, R., Liu, I., Arnold, R., and Fernandez, D. (2019). Bayesian model-based clustering for longitudinal ordinal data. Computational Statistics, 34(3), 1015-1038, DOI: 10.1007/s00180-019-00872-4.
- Berman, E., Chen, D. Y., Wang, X., and Liu, I. (2019). Executive entrepreneurship in national departments. Administration & Society, 51, 855-884. DOI: 10.1177/0095399717701523.
- Suesse, T. and Liu, I. (2019). Mantel-Haenszel estimators of a common odds ratio for multiple response data. Statistical Methods & Applications, 28, 57-76. DOI: 10.1007/s10260-018-0429-z.
- Fernandez, D., Arnold, R., Pledger, S., Liu, I., and Costilla, R. (2019). Finite mixture biclustering of discrete type multivariate data. Advances in Data Analysis and Classification, 13, 117-143. DOI: 10.1007/s11634-018-0324-3.
- Fernandez, D. and Liu, I. (2016). A goodness-of-fit test for the ordered stereotype model. Statistics in Medicine, 35(25), 4660-4696, DOI: 10.1002/sim.7002.
- Preedalikit, K., Liu, I., Hirose, Y. Sibanda, N. and Fernandez, D. (2016). Joint modeling of survival and longitudinal ordered data using a semiparametric approach. Australian & New Zealand Journal of Statistics 58, 153-172, DOI: 10.1111/anzs.12153.
- Matechou, E., Liu, I., Fernandez, D., Farias, M., and Gjelsvik, B. (2016). Biclustering models for two-mode ordinal data, Psychometrika 81:611, DOI:10.1007/s11336-016-9503-3.
- Liu, I. (2016). Analysis of Categorical Data with R by BILDER, C. R. and LOUGHIN, T. M. (Book Reviews). Australian and New Zealand Journal of Statistics, 58, 141-142.
- Xue, B., Lane, M., Liu, I., and Zhang, M. (2016). Dimension Reduction in Classification using Particle Swarm Optimisation and Statistical Variable Grouping Information. IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016). Athens, Greece, December 6-9, 2016 pp (to appear).
- Nguyen, H. B., Xue, B., Liu, I., Andreae, P., and Zhang, M. (2016). New mechanism for archive maintenance in PSO-based multi-objective feature selection. Soft Computing 20, 3927--3946 DOI 10.1007/s00500-016-2128-8.
- Nguyen, H. B., Xue, B., Liu, I., Andreae, P., and Zhang, M. (2015). Gaussian Transformation based Representation in Particle Swarm Optimisation for Feature Selection. Proceedings of the 18th European Conference on the Applications of Evolutionary Computation (EuroApplications 2015). Lecture Notes in Computer Science. Vol. 9028. Copenhagen, Denmark. 8-10 April 2015. pp. 541-553. (Nominated as Best Paper).
- Nguyen, H. B., Xue, B., Liu, I., and Zhang, M. (2014). PSO and Statistical Clustering for Feature Selection: A New Representation. In Simulated Evolution and Learning (pp. 569-581). Springer International Publishing.
- Arnold, R., Chukova, S., Hayakawa, Y. and Liu, I. (2014). Joint Modelling of Failure Times and Severities Using fuzzy Clustering. Proceedings of the Asia-Pacific International Workshop in Advanced Reliability Modelling, Sapporo, Japan, 21-23 August.
- Nguyen, H. B., Xue, B., Liu, I., and Zhang, M. (2014, July). Filter Based Backward Elimination in Wrapper Based PSO for Feature Selection in Classification. In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 3111-3118). IEEE.
- Lane, M. C., Xue, B., Liu, I., and Zhang, M. (2014). Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection. In Evolutionary Computation in Combinatorial Optimisation (pp. 133-144). Springer Berlin Heidelberg.
- Lane, M. C., Xue, B., Liu, I., and Zhang, M. (2013). Particle Swarm Optimisation and Statistical Clustering for Feature Selection. In AI 2013: Advances in Artificial Intelligence (pp. 214-220). Springer International Publishing
- Suesse, T. and Liu, I. (2013). Modelling strategies for
repeated multiple-response data. International Statistical Review 81, 230-248.
- Suesse, T. and Liu, I. (2012). Modelling strategies for
repeated multiple response data. Technical
Report,
MSOR 12-3 , Victoria University of Wellington.
- Suesse, T. and Liu, I. (2012). Mantel-Haenszel estimators
of odds ratios for stratified dependent binomial data. Computational
Statistics and Data Analysis 56, 2705-2717.
- Solomon, J., Jacobson, S. and Liu, I. (2012). Fishing for a solution: can collaborative resource management reduce poverty and support conservation? Environmental Conservation 39, 51-61.
- Liu, I. (2011). Analysis of Ordinal Categorical Data by
AGRESTI, A. (Book Reviews). Australian and New Zealand Journal of
Statistics 53, 124-126.
- Gunasekara, F., Carter, K., Liu, I., Richardson, K., and Blakely,
T. (2011). The relationship between income and health using
longitudinal data from New Zealand. Journal of Epidemiology and
Community Health, online: doi:10.1136/jech.2010.125021.
- Suesse, T. and Liu, I. (2011). Modelling strategies for
repeated multiple response data. Presented at the New Zealand
Statistical Association 2011 Conference in Auckland, New Zealand, 29 -
31August.
- Matechou, E. Liu, I., Pledger, S., and Arnold,
R. (2011). Biclustering models for ordinal data. Presented at the New
Zealand Statistical Association 2011 Conference in Auckland, New
Zealand, 29 - 31August.
- Liu, I. (2010). Marginal Models for Dependent, Clustered,
and Longitudinal Categorical Data by BERGSMA, W., CROON, M., and
HAGENAARS, J. A. (Book Reviews). Biometrics 66, 323-323.
- Gunasekara, F., Carter, K., Blakely, T., and Liu, I.
(2009). Determining the effect of change in income on self-rated
health using regression models - ordered, linear, fixed or random?
Working Paper to be presented at the British Household Panel Survey
Conference July 9-11th 2009, University of Essex,
Colchester. R Code
- Mukherjee, B. and Liu, I.. (2009). A Note on Bias Due to
Fitting Prospective Multivariate Generalized Liner Models to
Categorical Outcomes Ignoring Retrospective Sampling Schemes.
Journal of Multivariate Analysis, 100, 459-472.
- Liu, I.,, Mukherjee, B., Suesse, T., Sparrow, D., and
Park, S. K. (2009). Graphical Diagnostics to Check Model
Misspecification for the Proportional Odds Regression Model.
Statistics in Medicine, 28, 412-429.
- Suesse, T. and Liu, I. (2008). Diagnostics for Multiple
Response Data. The proceedings of PROBASTAT 2006 conference, Tatra
Mountains Mathematical Publications, 39, 105-113.
- Mukherjee, B., Ahn, J., Liu, I., Rathouz, P, and Sanchez,
B. (2008). Fitting stratified proportional odds models by amalgamating
conditional likelihoods. Statistics in Medicine, 27,
4950-4971.
- Liu, I. and Mukherjee, B. (2008). Proportional Odds
Models. Encyclopedia of Clinical Trials. (Editor:
R. D'Agostino, L. Sullivan, and J. Massaro). Wiley: New York.
- Liu, I. and Suesse, T. (2008). The Analysis of Stratified
Multiple Responses. Biometrical Journal, 50, 135-149.
- Liu, I., Suesse, T., and Mukherjee, B. (2007). Graphical
Model-Checking Methods for Proportional Odds Models. The Proceedings
of the International Statistical Institute 56th Session CD-ROM
(828.pdf). Lisbon, Portugal.
- Mukherjee, B., Liu, I., and Sinha, S. (2007). Analysis of
Matched Case-Control Data with Multiple Ordered Disease States:
Possible Choices and Comparisons. Statistics in Medicine, 26,
3240-3257.
- Liu, I. and Wang, D. (2007). Diagnostics for Stratified
Clinical Trials in Proportional Odds Models. Communications in
Statistics : Theory and Method, 36, 211-220.
- Liu, I. and Mukherjee, B. (2005). Possible Ways of Analysis
of Matched Case-Control Data with Multiple Ordered Disease
States. The Proceedings of the 40th Annual Conference of the
ORSNZ, (Wellington, New Zealand, December 2005), pp. 316-325.
- Liu, I. and Agresti, A. (2005). The Analysis of Ordered
Categorical Data: An Overview and a Survey of Recent
Developments. Test, 14, 1-73.
- Liu, I. (2005). Breslow-Day Test. Encyclopedia of
Biostatistics 2nd ed. (Editors: P. Armitage and T. Colton). Wiley:
London.
- Wang, D., Critchley, F. and Liu, I. (2004). Diagnostics
Analysis and Perturbations in a Clustered Sampling
Model. Communications in Statistics - Theory and Methods, 33,
2709-2721.
- Liu, I. (2003). Modeling and Describing Multiple Responses
Given a Stratification Variable. The Proceedings of the 54th
Session of the International Statistical Institute CD-ROM, Berlin,
Germany.
- Liu, I. (2003). Describing Ordinal Odds Ratios for
Stratified r x c Tables. Biometrical Journal, 45, 730-750.
- Hartzel, J., Liu, I. M. and Agresti, A. (2001). Describing
Heterogeneous Effects in Stratified Ordinal Contingency Tables, with
Application to Multi-Center Clinical Trials. Computational Statistics
and Data Analysis, 35, 429-449.
- Agresti, A. and Liu, I. M. (2001). Strategies for
Modeling a Categorical Variable Allowing Multiple Category
Choices. Sociological Methods and Research, 29, 403-434.
- Agresti, A. and Liu, I. M. (1999). Modeling a Categorical
Variable Allowing Arbitrarily Many Category
Choices. Biometrics, 55, 936-943.
- Liu, I. M. (1998). Breslow-Day Test. Encyclopedia of
Biostatistics (Editors: P. Armitage and T. Colton). Wiley: London.
- Liu, I. M. and Agresti, A. (1996). Mantel-Haenszel-Type
Inference for Cumulative Odds Ratios with a Stratified Ordinal
Response. Biometrics, 52, 1223-1234.
R Codes
Teaching
In 2023, I am teaching the following courses: