Jie Ding

Assistant Professor, College of Liberal Arts, Statistics (Twin Cities)

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Time series Engineering & Materials Science

Spectral density Engineering & Materials Science

Recovery Engineering & Materials Science

Splines Engineering & Materials Science

Model Comparison Mathematics

Adaptive filtering Engineering & Materials Science

Statistics Engineering & Materials Science

Object recognition Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2018 2019

1 Finished

Physics Inspired Score for Data-Driven Modeling and Prediction

Ding, J.

Duke University

9/26/18 → 6/23/19

Project: Research project

Data structures

Physics

Research Output 2012 2019

12 Conference contribution
12 Article

Asymptotically Optimal Prediction for Time-Varying Data Generating Processes

Ding, J., Zhou, J. & Tarokh, V., May 1 2019, In : IEEE Transactions on Information Theory. 65, 5, p. 3034-3067 34 p., 8543249.

Research output: Contribution to journal › Article

methodology

Kinetics

entropy

structural change

time series

2 Citations (Scopus)

Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency

Shao, S., Jacob, P. E., Ding, J. & Tarokh, V., Jan 1 2019, In : Journal of the American Statistical Association.

Research output: Contribution to journal › Article

Model Comparison

Bayesian Model

Bayes Factor

Scoring

Likelihood

Estimation of the evolutionary spectra with application to stationarity test

Xiang, Y., Ding, J. & Tarokh, V., Mar 1 2019, In : IEEE Transactions on Signal Processing. 67, 5, p. 1353-1365 13 p., 8598905.

Research output: Contribution to journal › Article

Spectral density

Fourier transforms

Analysis of Multistate Autoregressive Models

Ding, J., Shahrampour, S., Heal, K. & Tarokh, V., May 1 2018, In : IEEE Transactions on Signal Processing. 66, 9, p. 2429-2440 12 p.

Research output: Contribution to journal › Article

Time series

Markov processes

Experiments

Learning systems

Logistics

Neural networks