Bootstrap estimation of uncertainty in prediction for generalized linear mixed models

Flores-Agreda, Daniel; Cantoni, Eva

doi:10.1016/j.csda.2018.08.006

Cited by 14 publications

(8 citation statements)

References 45 publications

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“…It is likely that EC's relationship with the N constituents weakens as EC increases, given that many PWW constituents contribute to salinity of the water. These error ranges are larger than those calculated using classic methods (Flores-Agreda & Cantoni, 2019). Thus, accuracy gains simulated using these numbers can be considered conservative.…”

Section: N Application Uncertainty Using Ec-proxymentioning

confidence: 89%

“…The uncertainty of the EC-proxy measurements incorporated the heteroskedasticity of the models' residuals, which are not considered in classic methods for estimating the uncertainty of mixed model predictions (Flores-Agreda & Cantoni, 2019). In the context of the PWW lagoons, heteroskedasticity of the residuals is consistent with the limitations of EC as a proxy for the N constituents.…”

Section: N Application Uncertainty Using Ec-proxymentioning

confidence: 99%

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Optimizing accuracy of measurement protocols for nitrogen application in dilute dairy manure

Miller

Clark²,

Meyer

2022

J of Env Quality

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Variability of dairy process wastewater (PWW) nitrogen (PWW-N) content makes precise fertilizing of forage challenging. Our objective was to improve measurement accuracy of PWW-N. We characterized PWW-N and quantified its variability using over 1,000 PWW samples from 91 lagoons on commercial California dairies. We used statistical modeling and stochastic simulations to compare the accuracy of various protocols for measuring PWW-N applied to forage crops. Electrical conductivity (EC) was positively correlated with N concentration within a lagoon (conditional R 2 = .69, mixed model). Simulations compared the accuracy of N application rates when lagoon samples for N analyses were collected more frequently and from more homogeneous lagoons. When sampled quarterly, the N application measurement error was ±50%. Homogenizing PWW and sampling weekly reduced measurement error to ±42 and ±30%, respectively. Simulations using EC as an N concentration proxy within an automated irrigation system reduced N application measurement error to ±5%. More accurate automated N concentration measurements allow for precision N management that meets yield goals and reduces environmental effects.

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Section: N Application Uncertainty Using Ec-proxymentioning

confidence: 89%

Section: N Application Uncertainty Using Ec-proxymentioning

confidence: 99%

Optimizing accuracy of measurement protocols for nitrogen application in dilute dairy manure

Miller

Clark²,

Meyer

2022

J of Env Quality

View full text Add to dashboard Cite

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“…where f (x i ) is the forecasting values of the electricity-heat-cooling-gas load, f (x i ) is the actual regression values of the electricity-heat-cooling-gas load forecasting model, and ε(x i ) is the data noise [70].…”

Section: The Bootstrap Methodsmentioning

confidence: 99%

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Zhao

Guo

2021

Mathematics

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The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

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“…, K. This means that at least p100% of realizations of absolute prediction errors for all risk factors are smaller than or equal to QMAPE p (( Ni,T+1 ) K i=1 ). Let us introduce Algorithm 1, see [28][29][30], which will be used to estimate the prediction accuracy for the considered model, where the variability of random effects will be taken into account also for the risk factors not observed in the considered dataset.…”

Section: Bootstrap Estimators Of Prediction Accuracy Measures For Claim Frequencymentioning

confidence: 99%

The Measures of Accuracy of Claim Frequency Credibility Predictor

Wolny-Dominiak

Żądło

2021

Sustainability

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Nowadays, the sustainability risks and opportunities start to affect strongly insurance companies in regard to the resulting additional variability of future values of variables taken into account in the decision processes. This is important especially in the era of sustainable non-life insurance promoting, among others, the use of ecological car engines or ecological systems of building heating. The fundamental issue in non-life insurance is to predict future claims (e.g., the aggregate value of claims or the number of claims for a single policy) in a heterogeneous portfolio of policies taking account of claim experience. For this purpose, the so-called credibility theory is used, which was initiated by the fundamental Bühlmann model modified to the Bühlmann–Straub model. Several modifications of the model have been proposed in the literature. One of them is the development of the relationship between the credibility models and statistical mixed models (e.g., linear mixed models) for longitudinal data. The article proposes the use of the parametric bootstrap algorithm to estimate measures of accuracy of the credibility predictor of the number of claims for a single policy taking into account new risk factors resulting from the emergence of green technologies on the considered market. The predictor is obtained for the model which belongs to the class of Generalised Linear Mixed Models (GLMMs) and which is a generalization of the Bülmann–Straub model. Additionally, the possibility of predicting the number of claims and the problem of the assessment of the prediction accuracy are presented based on a policy characterized by new green risk factor (hybrid motorcycle engine) not previously present in the portfolio. The paper presents the proposed methodology in a case study using real insurance data from the Polish market.

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Bootstrap estimation of uncertainty in prediction for generalized linear mixed models

Cited by 14 publications

References 45 publications

Optimizing accuracy of measurement protocols for nitrogen application in dilute dairy manure

Optimizing accuracy of measurement protocols for nitrogen application in dilute dairy manure

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

The Measures of Accuracy of Claim Frequency Credibility Predictor

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