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Software

• [changepoints] an R package containing several methods for change point localization

Publications

(* indicates student collaborators)

1. Padilla, C.M.M.*, Xu,H., Wang, D., Padilla, O.H.M. and Yu, Y. (2023).
   Change point detection and inference in multivariable nonparametric models under mixing conditions.
   NeurIPS 2023. [pdf]


2. Li, W.*, Wang, D., and Rinaldo, A. (2023).
   Divide and conquer dynamic programming: an almost linear time change point detection methodology in high dimensions.
   ICML 2023. [pdf]


3. Padilla, C.M.M.*, Wang, D., Zhao, Z., and Yu, Y. (2022).
   Change-point detection for sparse and dense functional data in general dimensions.
   NeurIPS 2022. [pdf]


4. Li, W.*, Wang, D., and Rinaldo, A. (2022).
   Detecting abrupt changes in sequential pairwise comparison data.
   NeurIPS 2022. [pdf]


5. Wang, D., Yu, Y., and Willett, R. (2022).
   Detecting abrupt changes in high-dimensional self-exciting Poisson processes.
   Statistica Sinica, to appear. [pdf]


6. Wang, D., Zhao, Z., Yu, Y., and Willett, R. (2022).
   Functional linear regression with mixed predictors.
   Journal of Machine Learning Research, to appear. [pdf] [R code]


7. Yu, Y., Padilla, O.H.M., Wang, D., and Rinaldo, A. (2022).
   Network online change point localization.
   SIAM Journal on Mathematics of Data Science , to appear. [pdf]


8. Wang, D., Yu, Y., and Rinaldo, A. (2021).
   Optimal change point detection and localization in sparse dynamic networks.
   Annals of Statistics, 49.1 (2021): 203-232. [pdf] [R package]


9. Padilla, O.H.M., Yu, Y., Wang, D., and Rinaldo, A. (2021).
   Optimal nonparametric multivariate change point detection and localization.
   IEEE Transactions on Information Theory, 68.3 (2021): 1922-1944. [pdf] [R code]


10. Padilla, O.H.M., Yu, Y., Wang, D., and Rinaldo, A. (2021).
    Optimal nonparametric change point detection and localization.
    Electronic Journal of Statistics, to appear. [pdf] [R code]


11. Wang, D., Yu, Y., and Rinaldo, A. (2021).
    Optimal covariance change point localization in high dimensions.
    Bernoulli, 27.1 (2021): 554-575. [pdf]


12. Rinaldo, A., Wang, D., Wen, Q.*, Willett, R., and Yu, Y. (2021).
    Localizing changes in high-dimensional regression models.
    AISTATS 2021. [pdf] [R package]


13. Wang, D., Zhao, Z., Lin, K., and Willett, R. (2021).
    Statistically and computationally efficient change point localization in regression settings.
    Journal of Machine Learning Research, 22 (2021) 1-46. [pdf] [R code]


14. Wang, D., Yu, Y., and Rinaldo, A. (2020).
    Univariate mean change point detection: Penalization, CUSUM and optimality
    Electronic Journal of Statistics, 14.1: 1917-1961. [pdf] [R package]


15. Wang, D., Lu, X., and Rinaldo, A. (2019).
    DBSCAN: Optimal rates for density based clustering.
    Journal of Machine Learning Research, 20.170: 1-50. [pdf]


16. Ciollaro, M., Genovese, C., and Wang, D. (2016).
    Nonparametric clustering of functional data using pseudo-densities.
    Electronic Journal of Statistics, 10.2: 2922-2972. [pdf]


17. Perrotti, L., Walkington, N., and Wang, D. (2016).
    Numerical approximation of viscoelastic fluids.
    ESAIM: Mathematical Modeling and Numerical Analysis, 51.3: 1119-1144. [pdf]

Preprints

1. Khoo, Y., Peng, Y.*, and Wang, D. (2024).
   Nonparametric estimation via variance-reduced sketching.
   Preprint. [pdf]


2. Xu, H.*, Wang, D., Zhao, Z., and Yi, Y. (2022).
   Change point inference in high-dimensional regression models under temporal dependence.
   Preprint. [pdf]


3. Wang, D. and Zhao, Z. (2022)
   Optimal change-point testing for high-dimensional linear models with temporal dependence
   Preprint. [pdf]


4. Wang, D., Yu, Y., Rinaldo, A., and Willett, R. (2019).
   Localizing changes in high-dimensional vector autoregressive processes.
   Preprint. [pdf] [R code]


5. Wang, D., Zhao, Z., Willett, R., and Yau, C.Y., (2020).
   Functional autoregressive processes in reproducing kernel Hilbert spaces.
   Preprint. [pdf]


6. Chen, Y., Wang, D., Rinaldo, A., and Wasserman, L. (2016).
   Statistical analysis of persistence intensity functions.
   Preprint. [pdf]