Paper

Paper

$\dagger$ represents student author at the paper writing time.

Under working

  • Wu, K. and Politis, D.N., Calibration Prediction Interval for Non-parametric Regression and Neural Networks. (Submitted to Journal of Machine Learning Research) Paper Link.
  • Wu, K. and Politis, D.N., Deep Limit Model-free Prediction in Regression. (Submitted to ACM/IMS Journal of Data Science) Paper Link.
  • Ryan, O., Wu, K. and Jacobson, N.C., Exploratory Continuous-Time Modeling (expct): Extracting Dynamic Features from Irregularly Spaced Time Series. (Submitted to Multivariate Behavioural Research).
  • Wu, K., McFadden, J.R.$^{\dagger}$ and Jacobson, N.C., Determining Timing Effects of Microrandomized Trials Using Intensive Longitudinal Data and The Differential Time-varying Effect Model. (Under working) Paper Link.

Published

  • Wu, K., Karmakar, S. and Gupta, R., GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables. Journal of Forecasting, 2025. Paper Link.
  • Wu, K. and Politis, D.N., Scalable Subsampling Inference of Deep Neural Networks. ACM/IMS Journal of Data Science 2025, 2(1), 1-29. Paper Link.
  • Wu, K.and Politis, D.N., Bootstrap Prediction Inference of Nonlinear Autoregressive Models, Journal of Time Series Analysis 2024, 45, 800-822. Paper Link.
  • Wu, K., Gupta, R., Pierdzioch, C. and Karmakar, S., Climate Risks and Stock Market Volatility over A Century in An Emerging Market Economy: The Case of South Africa. Climate 2024, 12(5), 68. Paper Link.
  • Politis, D.N. and Wu, K., Non-parametric Forward Bootstrap on Predicting Non-linear Time Series: Consistency, Pertinence and Debiasing, Stats 2023, 6(3), 839-867. Paper Link.
  • Wu, K. and Karmakar, S., A Model-free Approach to Do Long-term Volatility Forecasting and Its Variants, Financial Innovation 2023, 9(59). Paper Link.
  • Wu, K. and Karmakar, S., Model-Free Time-aggregated Predictions for Econometric Datasets, Forecasting 2021, 3(4), 920-933. Paper Link.

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