The Whittle likelihood for mixed models with application to groundwater level time series

TL;DR

This paper introduces a frequency-domain approach using the Whittle likelihood to efficiently estimate all parameters in mixed models for groundwater level time series, even with large datasets and non-Gaussian data.

Contribution

We develop a joint estimation method for mixed model parameters using the Whittle likelihood, improving computational efficiency and robustness over traditional maximum likelihood methods.

Findings

Method handles large datasets efficiently.

Robust to missing and non-Gaussian data.

Outperforms maximum likelihood in simulations and real data.

Abstract

Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, which combine a fixed mean, expressed using independent predictors, with autocorrelated random errors, are used for inference, forecasting and filling in missing values in groundwater level time series. Estimating parameters of mixed models using maximum likelihood has high computational complexity. For large datasets, this leads to restrictive simplifying assumptions such as fixing certain free parameters in practical implementations. In this paper, we propose a method to jointly estimate all parameters of mixed models using the Whittle likelihood, a frequency-domain quasi-likelihood. Our method is robust to missing and non-Gaussian data and can handle much larger data sizes. We demonstrate the utility of our method both in a simulation study and with real-world data, comparing against maximum likelihood and an alternative two-stage approach that estimates fixed and random effect parameters separately.