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Software

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

Publications

(* indicates student collaborators)

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


• Padilla, O.H.M., Yu, Y., Wang, D., and Rinaldo, A. (2022).
  Optimal nonparametric multivariate change point detection and localization.
  IEEE Transactions on Information Theory, to appear. [pdf] [R code]


• 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]


• 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, to appear. [pdf]


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


• 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]


• 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]


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


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


• 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]


• 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]


• 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]


• 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]


• 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

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


• Wang, D. and Zhao, Z. (2022)
  Optimal Change-point Testing for High-dimensional Linear Models with Temporal Dependence
  Preprint. [pdf]


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


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


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