Environmetrics最新文献

Newlands, N.K. and Lyubchich, V. 2025. “ Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models.” Environmetrics 36(2), e70000. https://doi.org/10.1002/env.70000.

In the initial published version of this article, the title was incorrect. Below is the corrected article title:

Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”

We apologize for this error.

In this article, the relationship between the monthly precipitation in 30 European cities and towns, and two Algerian ones, and the North Atlantic Oscillation (NAO) index is characterized using the Vector Seasonal Shifting Mean and Covariance Autoregressive model, extended to contain exogenous variables. The results, based on monthly time series from 1851 up until 2020, include shifting monthly means for the rainfall series and the estimated coefficients of the exogenous NAO variable. They suggest that in the north and the west, the amount of rain in the boreal winter months has increased or stayed the same during the observation period, whereas in the Mediterranean area, there have been systematic decreases. Results on the North Atlantic Oscillation indicate that the NAO has its strongest effect on precipitation during the winter months. The (negative) effect is particularly strong in Western Europe, Lisbon, and the Mediterranean rim. In contrast, the effect in northern locations is positive for the winter months. The constancy of error variances and correlations is tested and, if rejected, the time-varying alternative is estimated. A spatial relationship between the error correlations and the distance between the corresponding pairs of cities is estimated.

We contribute to the discussion of the insightful article “Assessing predictability of environmental time series with statistical and machine learning models” by Bonas et al. (2024), in which the authors commend their effort in comparing a wide range of methodologies for the challenging task of predicting environmental time series data. We focus our discussion on two topics of interest to us. First, we consider extensions of the explored methodologies that allow for heteroscedastic error terms. Second, we consider non-Gaussianity and fitting models on transformed data. For both of these points, we will make use of the authors' supplied code and data in order to extend their examples. Ultimately, we find that modeling of heteroscedasticity error terms has the potential to improve both point and interval estimates for these environmental time series. We also find that the use of transformations to handle non-Gaussianity can improve interval estimates.

In dose-response modeling, several models can often yield satisfactory fits to the observed data. The current practice in risk assessment is to use model averaging, which is a way to combine multiple models in a weighted average. A key parameter in risk assessment is the benchmark dose, the dose resulting in a predefined abnormal change in response. Current practice when applying frequentist model averaging is to use weights based on the Akaike Information Criterion (AIC). This paper introduces stacking weights as an alternative for dose-response modeling and generalizes a Diversity Index from dichotomous to continuous responses for model space selection. Three simulation studies were conducted to evaluate the new methods. They showed that, in three realistic scenarios, recommended strategies generally performed well, with stacking weights outperforming AIC weights in several cases. Strategies involving model selection were less effective. However, in a challenging scenario, none of the methods performed well. Due to the promising results of stacking weights, they have been added to the R package “bmd.”

The relative merits of machine learning and statistical methods are discussed recently by Bonas et al. 2004, who raise important open questions for the statistical community regarding the value-added benefits of data science and the future role of environmental statistics. Specifically, they identify three major knowledge gaps where statistics is seen as crucial to strengthening inference in machine learning (ML): to provide an ML model-based framework amenable to explainability, to determine the best approach for quantifying uncertainty in relation to complex environmental dynamics, and to comprehensively identify ML's value-added benefits. We continue this discussion by exploring these general questions and sharing our perspective and insights from our modeling of marine and terrestrial ecosystem dynamics. We propose several lines of inquiry where environmental statisticians and data scientists could collaboratively advance predictive analytics.

Focusing on a sample of 39 countries in the period 1996–2017, we analyze whether the relationship between environmental taxes and CO₂ emissions depends on the quality of political institutions. Our results show that an increase in the environmental tax revenue is related to a reduction in CO₂ emissions only in countries with more consolidated democratic institutions, higher civil society participation, and less corrupt governments. Moreover, the relationship between CO₂ emissions and revenue neutral shifts to different tax sources depends not only on the quality of political institutions, but also on the kind of externality the policymaker aims at correcting.

In environmental science, practitioners often deal with data recorded sequentially along time, such as time series of wind direction or wind speed. In this context, clustering of time series is a useful tool to carry out exploratory analyses. While most of the proposals are focused on real-valued time series, very few works consider circular time series, despite the frequent appearance of these objects in many disciplines. In this manuscript, a dissimilarity for circular time series is introduced and used in combination with a soft clustering method. The metric relies on a measure of serial dependence considering circular arcs, thus taking advantage of the directional character inherent to the series range. The clustering approach is able to group together time series generated from similar stochastic processes. Some simulations show that the method exhibits a reasonable clustering effectiveness, outperforming alternative techniques in many contexts. Two interesting applications involving time series of wind direction in Saudi Arabia show the potential of the proposed approach.