Research

Research

And what, Socrates, is the food of the soul? Surely, I said, knowledge is the food of the soul.

— Plato

Paper

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

Under working

  • Wu, K. Sundararajan,R., Haddad, M., Piancastelli, L., Barreto-Souza and W., Mixed Time Series Quasi-Likelihood Models for Uncovering Covid-19 Viral Load and Mortality Dynamics. (Submitted to Journal of the Royal Statistical Society Series C) Paper Link.
  • Wu, K. and Politis, D.N., Calibration Prediction Interval for Non-parametric Regression and Neural Networks. (Submitted to Electronic Journal of Statistics) 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

  1. Wu, K., Karmakar, S. and Gupta, R., GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables. Journal of Forecasting, 2025. Paper Link
  2. 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
  3. Wu, K. and Politis, D. N., Bootstrap Prediction Inference of Nonlinear Autoregressive Models. Journal of Time Series Analysis, 2024, 45, 800–822. Paper Link
  4. 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
  5. 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
  6. 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
  7. Wu, K. and Karmakar, S., Model-Free Time-aggregated Predictions for Econometric Datasets. Forecasting, 2021, 3(4), 920–933. Paper Link

Conference

  1. IMS International Conference on Statistics and Data Science Conference, Seville, Spain, 2025
    Deep Limit Model Free Prediction in Regression, Talk
  2. NBER-NSF Time Series Conference, Rutgers University, U.S.A., 2025
    Types of Distribution-free Methods for Forecasting Financial Volatility, Poster
  3. NBER-NSF Time Series Conference, Rutgers University, U.S.A., 2025
    Bootstrap Prediction Inference of Non-linear Autoregressive Models, Co-authored Talk
  4. Statistical Frontiers in LLMs and Foundation Models — NeurIPS, Vancouver, Canada, 2024
    Deep Limit Model-free Prediction, Poster
  5. Statistical Frontiers in LLMs and Foundation Models — NeurIPS, Vancouver, Canada, 2024
    Subsampling on Deep Neural Networks, Poster
  6. Computational and Methodological Statistics, Virtual, 2022
    Extracting Dynamic Features from Irregularly Spaced Time Series, Co-authored Talk

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