Forskning
Multivariate analysis is a powerful tool in statistical research, enabling the examination of complex data involving multiple variables simultaneously. This approach is essential in various fields where understanding the relationships between multiple factors is key to drawing meaningful conclusions. Multivariate analysis plays a critical role in some of the primary research areas such as econometrics, psychometrics, environmental science, marketing and consumer research, genomics and bioinformatics, and social sciences to name a few.
Due to the fast development of the Internet of Things (IoT), the availability of data increasing drastically. It brings more challenges and opportunities: the high-dimensional statistical analysis. Under the context of high-dimensional data, some new and more powerful methods need to be investigated to fit the new needings.
My research interests start with developing methods under high-dimensional settings. I'm also interested in the empirical applications of machine learning/data science with energy industry-related topics.
Vi är kvalitetscertifierade
Ekonomihögskolan och Linnéuniversitetet är ackrediterade av The Association to Advance Collegiate Schools of Business, AACSB.
Mina forskargrupper
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Deterministic and Stochastic Modelling Forskningsområdet Deterministic and Stochastic Modelling inom Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) samlar forskare med…
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Linnaeus University Centre for Data Intensive Sciences and Applications DISA är Linnéuniversitetets spetsforskningsmiljö som arbetar med insamling, analys och nyttogörande av stora datamängder.…
Mina pågående forskningsprojekt
Publikationer
Artikel i tidskrift (Refereegranskat)
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Dai, D., Hao, C., Jin, S., Liang, Y. (2023). Regularized estimation of Kronecker structured covariance matrix using modified Cholesky decomposition. Journal of Statistical Computation and Simulation.
Status: Epub för tryck -
Dai, D., Pan, J., Liang, Y. (2022). Regularized estimation of the Mahalanobis distance based on modified Cholesky decomposition. Communications in Statistics: Case Studies, Data Analysis and Applications. 8 (4). 559-573.
Status: Publicerad -
Dai, D., Liang, Y. (2021). High-Dimensional Mahalanobis Distances of Complex Random Vectors. Mathematics. 9 (16).
Status: Publicerad -
Yan, J., Karlsson, A., Zou, Z., Dai, D., Edlund, U. (2020). Contamination of heavy metals and metalloids in biomass and waste fuels : Comparative characterisation and trend estimation. Science of the Total Environment. 700. 1-19.
Status: Publicerad -
Dai, D. (2020). Mahalanobis distances on factor model based estimation. Econometrics. 8 (1). 1-11.
Status: Publicerad -
Dai, D., Holgersson, T., Karlsson, P.S. (2017). Expected and unexpected values of Individual Mahalanobis Distances. Communications in Statistics - Theory and Methods. 46 (18). 8999-9006.
Status: Publicerad -
Gregebo, B., Dai, D., Schillberg, B., Baehr, M., Nyström, B., et al. (2014). Private and non-private disc herniation patients : do they differ?. The Open Orthopaedics Journal. 8. 237-241.
Status: Publicerad
Konferensbidrag (Refereegranskat)
- Dai, D., Holgersson, T. (2018). High-Dimensional CLTs for Individual Mahalanobis Distances. Trends and perspectives in linear statistical inference : proceedings of the LINSTAT2016 meeting held 22-25 August 2016 in Istanbul, Turkey. 57-68.
Kapitel i bok, del av antologi (Refereegranskat)
- Liang, Y., Hao, C., Dai, D. (2024). Two-sample intraclass correlation coefficient tests for matrix-valued data. Statistical Modeling and Applications : Heavy-Tailed, Skewed Distributions and Mixture Modeling, Volume 2. Springer.
- Liang, Y., Dai, D. (2020). On Explicit Estimation of the Growth Curve Model with a Block Circular Covariance Structure. Recent Developments in Multivariate and Random Matrix Analysis : Festschrift in Honour of Dietrich von Rosen. Springer. 255-266.