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Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse... Read more
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse problems in physical oceanography, oceanography, trajectory analysis in life course epidemiology ; (3) Inferential Statistics: efficient estimation for semiparametric models, empirical likelihood, knockoff statistics, model selection, non-standard asymptotic theory, post-selection inference; (4) Statistical Genetics Genomics Omics: analysis of whole-genome sequencing data, integrative analysis of multi-omics data; (5) Survival and Event History Analysis: competing risks models for HIV/AIDS data, counting process and martingale methods, network survival model; (6) Time Series Analysis: inference for stochastic processes, risk management, statistical finance.
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse... Read more
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse problems in physical oceanography, oceanography, trajectory analysis in life course epidemiology ; (3) Inferential Statistics: efficient estimation for semiparametric models, empirical likelihood, knockoff statistics, model selection, non-standard asymptotic theory, post-selection inference; (4) Statistical Genetics Genomics Omics: analysis of whole-genome sequencing data, integrative analysis of multi-omics data; (5) Survival and Event History Analysis: competing risks models for HIV/AIDS data, counting process and martingale methods, network survival model; (6) Time Series Analysis: inference for stochastic processes, risk management, statistical finance.
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
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PhD (Biostatistics)
City University of Hong Kong, Kowloon,, Hong Kong SAR
# =60QS Subject Rankings
24 monthsProgram duration
MathematicsMain Subject Area
Program overview
Main Subject
Mathematics
Degree
PhD
Study Level
PHD
Study Mode
On Campus
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse...
Read more
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse problems in physical oceanography, oceanography, trajectory analysis in life course epidemiology ; (3) Inferential Statistics: efficient estimation for semiparametric models, empirical likelihood, knockoff statistics, model selection, non-standard asymptotic theory, post-selection inference; (4) Statistical Genetics Genomics Omics: analysis of whole-genome sequencing data, integrative analysis of multi-omics data; (5) Survival and Event History Analysis: competing risks models for HIV/AIDS data, counting process and martingale methods, network survival model; (6) Time Series Analysis: inference for stochastic processes, risk management, statistical finance.
Read lessProgram overview
Main Subject
Mathematics
Degree
PhD
Study Level
PHD
Study Mode
On Campus
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse...
Read more
Research focus/expertise in the Department include: (1) Computational Statistics: Bayesian hierarchical models, empirical Bayes, large-scale data analysis, Markov chain Monte Carlo, statistical machine Learning; (2) Functional Data Analysis: Bayesian inverse problems in physical oceanography, oceanography, trajectory analysis in life course epidemiology ; (3) Inferential Statistics: efficient estimation for semiparametric models, empirical likelihood, knockoff statistics, model selection, non-standard asymptotic theory, post-selection inference; (4) Statistical Genetics Genomics Omics: analysis of whole-genome sequencing data, integrative analysis of multi-omics data; (5) Survival and Event History Analysis: competing risks models for HIV/AIDS data, counting process and martingale methods, network survival model; (6) Time Series Analysis: inference for stochastic processes, risk management, statistical finance.
Read lessAdmission Requirements
Scholarships
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QS subject rankings
Subject: Mathematics
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