A Unified Framework for Bridging the Gap between Nonparametric and Parametric Inferences based on Ranking Data


Grant Data
Project Title
A Unified Framework for Bridging the Gap between Nonparametric and Parametric Inferences based on Ranking Data
Principal Investigator
Dr Yu, Philip Leung Ho   (Principal investigator)
Co-Investigator(s)
Professor Alvo Mayer   (Co-Investigator)
Duration
36
Start Date
2015-09-01
Completion Date
2018-08-31
Amount
631972
Conference Title
Presentation Title
Keywords
Ranking data, Statistical modeling, Nonparametric inference, Block designs, Incomplete ranking
Discipline
Probability & Statistics
Panel
Physical Sciences (P)
Sponsor
RGC General Research Fund (GRF)
HKU Project Code
17303515
Grant Type
General Research Fund (GRF)
Funding Year
2015/2016
Status
On-going
Objectives
2 We will extend the models to incorporate incomplete ranking data and covariates. When the dimension of the rank score vector is high, we may consider penalized likelihood estimation in order to narrow down the number of rank scores. 3 Applications to real-world ranking data sets will be considered during the development process of the proposed model and nonparametric methods. Software that will be useful for researchers will be developed.