4 papers
Prediction Inference of Time Series with Standard ReLU Deep Neural Networks
Kejin Wu
We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty. Classically, people rely on linear, non-linear, o…
Mixed Time Series Quasi-Likelihood Models for Uncovering Covid-19 Viral Load and Mortality Dynamics
Kejin Wu, Raanju R. Sundararajan, Michel F. C. Haddad +2
Accurate real-time monitoring of disease transmission is crucial for epidemic control, which has conventionally relied on reported cases or hospital admissions. Such metrics are fr…
Multi-step ahead prediction intervals for non-parametric autoregressions via bootstrap: consistency, debiasing and pertinence
Dimitris N. Politis, Kejin Wu
To address the difficult problem of multi-step ahead prediction of non-parametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, w…
GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables
Kejin Wu, Sayar Karmakar, Rangan Gupta
In this work, we explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transformation with the possible inclusion of exogenous variabl…