Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

Hu, Shuwen; Wang, You‐Gan; Drovandi, Christopher; Cao, Taoyun

doi:10.1007/s10260-022-00658-x

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Our approach can be used anytime we cast some doubts about the Gaussianity of the random effects, or simply whenever we aim at a flexible modeling with no assumptions on their distribution. There has been a wide and partially controversial literature on the diagnosis of random effects misspecification in mixed models (see e.g., Bartolucci et al, 2017; Huang, 2009) and what its impacts are on estimation and inference (see e.g., the recent contributions by Hu et al, 2022 and Schumacher et al, 2021). The debate is still ongoing and includes results indicating a considerable loss of efficiency (Agresti et al, 2004) and others that instead conclude how the wrong Gaussian assumption is not as harmful (McCulloch & Neuhaus, 2011).…”

Section: Discussionmentioning

confidence: 99%

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Maruotti

Loro

2023

Environmetrics

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We introduce a bivariate bidimensional mixed‐effects regression model, motivated by the analysis of CO2$$ {\mathrm{CO}}_2 $$ emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.

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Section: Discussionmentioning

confidence: 99%

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Maruotti

Loro

2023

Environmetrics

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“…There were several missing data in the dataset (13%). The dataset was thus rebalanced by model-based imputation using the random forest imputation method 76 , 77 .…”

Section: Methodsmentioning

confidence: 99%

Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods

Hahn,

Kurtz,

de Paula

et al. 2024

Sci Rep

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While onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil. The complexity of onion cropping systems was captured by machine learning (ML) methods. The RReliefF ranking algorithm showed that the split-N dosage and soil tests for micronutrients and S were the most relevant features to predict bulb yield. The decision-tree random forest and extreme gradient boosting models were accurate to predict bulb yield from the relevant predictors (R2 > 90%). As shown by the gain ratio, foliar nutrient standards for nutritionally balanced and high-yielding specimens producing > 50 Mg bulb ha−1 set apart by the ML classification models differed among cultivars. Cultivar × environment interactions support documenting local nutrient diagnosis. The split-N dosage was the most relevant controllable feature to run future universality tests set to assess models’ ability to generalize to growers’ fields.

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“…Those learning techniques can process large amounts of data. The nature of the data is more important than the learning technique [30]. Databases can be balanced or not.…”

Section: Introductionmentioning

confidence: 99%

Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features

Parent,

Deslauriers

2023

Nitrogen

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Maize (Zea mays) is a high-nitrogen (N)-demanding crop potentially contributing to nitrate contamination and emissions of nitrous oxide. The N fertilization is generally split between sowing time and the V6 stage. The right split N rate to apply at V6 and minimize environmental damage is challenging. Our objectives were to (1) predict maize response to added N at V6 using machine learning (ML) models; and (2) cross-check model outcomes by independent on-farm trials. We assembled 461 N trials conducted in Eastern Canada between 1992 and 2022. The dataset to predict grain yield comprised N dosage, weekly precipitations and corn heat units, seeding date, previous crop, tillage practice, soil series, soil texture, organic matter content, and pH. Random forest and XGBoost predicted grain yield accurately at the V6 stage (R2 = 0.78–0.80; RSME and MAE = 1.22–1.29 and 0.96–0.98 Mg ha−1, respectively). Model accuracy up to the V6 stage was comparable to that of the full-season prediction. The response patterns simulated by varying the N doses showed that grain yield started to plateau at 125–150 kg total N ha−1 in eight out of ten on-farm trials conducted independently. There was great potential for economic and environmental gains from ML-assisted N fertilization.

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Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

Cited by 7 publications

References 38 publications

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods

Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features

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Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

Cited by 7 publications

References 38 publications

CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model

CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model

Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods

Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features

Contact Info

Product

Resources

About

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model