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Published in 2017 at "Journal of Geophysical Research"
DOI: 10.1002/2017jd026648
Abstract: Accurate estimation of precipitation from satellites at high spatiotemporal scales over the Tibetan Plateau (TP) remains a challenge. In this study, we proposed a general framework for blending multiple satellite precipitation data using the dynamic…
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Keywords:
plateau;
precipitation;
multisatellite precipitation;
model averaging ... See more keywords
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Published in 2024 at "Applied Stochastic Models in Business and Industry"
DOI: 10.1002/asmb.2898
Abstract: In this article, we present a novel approach for estimating the conditional average treatment effect in models with binary responses. Our proposed method involves model averaging, and we establish a weight choice criterion based on…
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Keywords:
treatment effects;
model averaging;
treatment;
averaging estimating ... See more keywords
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Published in 2024 at "Statistics in Medicine"
DOI: 10.1002/sim.10309
Abstract: A common problem in numerous research areas, particularly in clinical trials, is to test whether the effect of an explanatory variable on an outcome variable is equivalent across different groups. In practice, these tests are…
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Keywords:
model averaging;
equivalence;
overcoming model;
model ... See more keywords
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Published in 2017 at "Statistics in medicine"
DOI: 10.1002/sim.7395
Abstract: Assessing the QT prolongation potential of a drug is typically done based on pivotal safety studies called thorough QT studies. Model-based estimation of the drug-induced QT prolongation at the estimated mean maximum drug concentration could…
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Keywords:
concentration response;
model averaging;
method;
model ... See more keywords
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Published in 2020 at "Science China Information Sciences"
DOI: 10.1007/s11432-019-2705-2
Abstract: Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible…
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Keywords:
collaborative deep;
multiple data;
model averaging;
deep learning ... See more keywords
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Published in 2018 at "Economic Modelling"
DOI: 10.1016/j.econmod.2018.07.009
Abstract: Abstract We show in this paper why researchers ought to pay particular attention to the issues of model uncertainty and data poolability in their panel data applications. We focus on the identification of robust determinants…
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Keywords:
panel data;
bayesian model;
model averaging;
model ... See more keywords
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Published in 2017 at "Field Crops Research"
DOI: 10.1016/j.fcr.2017.06.011
Abstract: Abstract Process-based crop models are popular tools to evaluate the impact of climate change and agricultural management on crop growth. Accurate simulation of crop production over large geographic regions using an individual crop model remains…
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Keywords:
bayesian model;
crop model;
model averaging;
model ... See more keywords
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Published in 2021 at "International Journal of Forecasting"
DOI: 10.1016/j.ijforecast.2020.12.004
Abstract: Abstract This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first…
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Keywords:
survival response;
model;
optimal model;
model averaging ... See more keywords
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Published in 2020 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2020.09.008
Abstract: Abstract Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on…
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Keywords:
dichotomous response;
model averaging;
semiparametric model;
model ... See more keywords
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Published in 2021 at "Journal of Statistical Planning and Inference"
DOI: 10.1016/j.jspi.2021.08.003
Abstract: Abstract In this paper, we consider model averaging for expectile regressions, which are common in many fields. The J -fold cross-validation criterion is developed to determine averaging weights. Under some regularity conditions, we prove that…
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Keywords:
averaging estimator;
expectile regressions;
model averaging;
model ... See more keywords
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Published in 2019 at "Probabilistic Engineering Mechanics"
DOI: 10.1016/j.probengmech.2019.02.002
Abstract: Abstract Kriging metamodels are widely used to approximate the response of computationally-intensive engineering models for a variety of applications, ranging from system design to uncertainty quantification. Their predictions are formulated by combining a regression that…
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Keywords:
regression;
basis functions;
averaging kriging;
model ... See more keywords