Basic statistics I (1ST401,1ST512)
Basic statistics II (1ST061,1ST904)
Multivariate statistical analysis and its related methods are widely used in various areas when one gets more than one variable in the analysis. Many multivariate analyses assume that the random variables are normally distributed. But this restriction is violated frequently in the reality due to some practical issues, for example, the outlier. Therefore, outlier detection has become an important and necessary step in all kinds of analysis. Much attention was paid in developing different methods to detect the outlier more efficiently during the last few decades.
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 are needed to be investigated to fit the new needings.
My research interests start with developing the outlier detection methods, for example, Mahalanobis distance under high dimensional settings. Mahalanobis distance consists of two parts: mean and covariance. These two parameters are used not only to build the Mahalanobis distance but also to calibrate various statistics such as the Normal distribution and t-test statistics, to name but a few. From this point, my research is extended to different covariance matrix estimation related methods especially for the high-dimensional setting, here high dimensional implies when the number of variables is larger than the number of observations. I'm also interested in the empirical applications of machine learning/data science with energy industry-related topics.
Vi är kvalitetscertifierade
Ekonomihögskolan vid Linnéuniversitetet är ackrediterade av The Association to Advance Collegiate Schools of Business; AACSB.
Deterministic and Stochastic Modelling Forskningsområdet Deterministic and Stochastic Modelling inom Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) samlar forskare med…
Kunskapsplattform: Sustainable co-creation – through interdisciplinary solutions Information om kunskapsplattformen finns på vår engelska sida.
Mina pågående forskningsprojekt
Artikel i tidskrift (Refereegranskat)
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.
Dai, D., Liang, Y. (2021). High-Dimensional Mahalanobis Distances of Complex Random Vectors. Mathematics. 9 (16).
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.
Dai, D. (2020). Mahalanobis distances on factor model based estimation. Econometrics. 8 (1). 1-11.
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.
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.
- 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., 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.
Konferensbidrag (Övrigt vetenskapligt)
Kapitel i bok, del av antologi (Övrigt vetenskapligt)
- Dai, D. (2015). Mahalanobis distances of factor structured data. Festschrift in honor of Professor Ghazi Shukur on the occasion of his 60th birthday. Linnaeus University Press. 126-142.
Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
- Dai, D. (2014). Moments, factor scores and limiting distributions of individual Mahalanobis distances. Licentiate Thesis. Växjö, Department of Economics and Statistics, Linnaeus University. 89.