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Nonlinear modal regression for dependent data with application for predicting COVID-19.

Aman Ullah1, Tao Wang1,2, Weixin Yao3

  • 1Department of Economics University of California Riverside California USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|September 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new nonlinear modal regression for time series data, showing it accurately predicts COVID-19 trends. The model offers improved fitting and precise forecasts compared to traditional methods.

Keywords:
COVID‐19MEM algorithmdependent datamodal regressionnonlinearprediction

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Area of Science:

  • Statistics
  • Time Series Analysis
  • Econometrics

Background:

  • Time series data often exhibits complex dependencies.
  • Existing nonlinear regression models may not fully capture modal behavior in time series.
  • Accurate modeling of disease spread, like COVID-19, is crucial for public health policy.

Purpose of the Study:

  • To develop and validate a novel nonlinear modal regression for dependent time series.
  • To establish the theoretical properties (consistency, asymptotic distribution) of the proposed estimator.
  • To apply the model for forecasting COVID-19 new cases and deaths in the US.

Main Methods:

  • Development of a nonlinear modal regression estimator for α-mixing dependent samples.
  • Theoretical analysis of consistency and asymptotic properties with shrinking bandwidth.
  • Numerical estimation using a modified modal expectation-maximization (MEM) algorithm and Taylor expansion.
  • Application to COVID-19 data for state/region-level forecasting.

Main Results:

  • The nonlinear modal estimator is consistent with a slower convergence rate than mean regression.
  • The modified MEM algorithm effectively estimates the model parameters.
  • The proposed model demonstrates superior performance in fitting and predicting COVID-19 data compared to traditional nonlinear regressions.
  • Predictions reveal significant regional variations in COVID-19 spread across US states.

Conclusions:

  • The developed nonlinear modal regression is a robust tool for analyzing dependent time series data.
  • The model provides more accurate predictions for epidemic data, aiding public health decision-making.
  • Understanding regional differences in disease spread is essential for targeted interventions.