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Model Averaging in Economics [PDF]
SSRN Electronic Journal, 2011 Model uncertainty remains a challenge to applied researchers in economics. When many competing models are available for estimation and without enough guidance from theory, model averaging represents an alternative to model selection. Despite model averaging approaches have been present in statistics for many years, only over the recent decades are ...
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Model‐Averaged Confidence Intervals [PDF]
Scandinavian Journal of Statistics, 2015 AbstractWe develop an approach to evaluating frequentist model averaging procedures by considering them in a simple situation in which there are two‐nested linear regression models over which we average. We introduce a general class of model averaged confidence intervals, obtain exact expressions for the coverage and the scaled expected length of the ...
Kabaila, Paul +2 more
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Sensors, 2006 The performance of weak gaseous plume-detection methods in hyperspectral long-wave infrared imagery depends on scene-specific conditions such at the ability to properly estimate atmospheric transmission, the accuracy of estimated chemical signatures, and
Nicolas Hengartner, Tom Burr
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Sensors, 2022 Application of bio-based fertilizers is considered a practical solution to enhance soil fertility and maintain soil quality. However, the composition of bio-based fertilizers needs to be quantified before their application to the soil.
Khan Wali +4 more
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Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities [PDF]
, 2008 We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging(BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper's BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to ...
Giorgio Corani, Marco Zaffalon
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Model averaging for generalized linear models in fragmentary data prediction
Statistical Theory and Related Fields, 2022 Fragmentary data is becoming more and more popular in many areas which brings big challenges to researchers and data analysts. Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response ...
Chaoxia Yuan, Yang Wu, Fang Fang
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A New Model Averaging Approach in Predicting Credit Risk Default
Risks, 2021 The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach.
Paritosh Navinchandra Jha +1 more
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RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition
Frontiers in Public Health, 2021 Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12 ...
Shui-Hua Wang +4 more
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Capture‐recapture using multiple data sources: estimating the prevalence of diabetes
Australian and New Zealand Journal of Public Health, 2012 Objective: To examine the potential for using multiple list sources and capture‐recapture methods for estimating the prevalence of diagnosed diabetes. Method: A model‐averaging procedure using an adjusted Akaike's Information Criterion (QAICc) was used ...
Claire M. Cameron +3 more
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Plants, 2019 Physiological and morphological traits have a considerable impact on the biomass production of fast-growing trees. To compare cultivar difference in shoot biomass and investigate its relationships with leaf functional traits in mulberry, agronomic traits
Xu Cao +5 more
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