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Estimating average treatment effect by model averaging
Economics Letters, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yichen Gao, Wei Long, Zhengwei Wang
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2013
This chapter deals with methodologies of obtaining the so-called averaged model, which focuses on capturing the low-frequency behavior of power electronic converters while neglecting high-frequency variations due to circuit switching. This appears to be a natural action, as every converter employs filters in order to limit the ripple of various ...
Seddik Bacha +2 more
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This chapter deals with methodologies of obtaining the so-called averaged model, which focuses on capturing the low-frequency behavior of power electronic converters while neglecting high-frequency variations due to circuit switching. This appears to be a natural action, as every converter employs filters in order to limit the ripple of various ...
Seddik Bacha +2 more
openaire +1 more source
On threshold moving‐average models [PDF]
Journal of Time Series Analysis, 1998 In this paper the class of discrete self‐exciting threshold moving‐average (SETMA) models is studied in some detail. In particular, we consider various problems associated with the identification, estimation and testing of these models. A simple method for distinguishing between low order moving average (MA) and low order SETMA models is presented ...
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Circuit Averaging, Averaged Switch Modeling, and Simulation
2020Circuit averaging is another well-known technique for derivation of converter equivalent circuits. Rather than averaging the converter state equations, with the circuit averaging technique we average the converter waveforms directly. All manipulations are performed on the circuit diagram, instead of on its equations, and hence the circuit averaging ...
Robert W. Erickson, Dragan Maksimović
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Ensemble averaging in turbulence modelling
Physics Letters A, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Grmela, Miroslav +4 more
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2018
Model averaging is a means of allowing for model uncertainty in estimation which can provide better estimates and more reliable confidence intervals than model selection. We illustrate its use via examples involving real data, discuss when it is likely to be useful, and compare the frequentist and Bayesian approaches to model averaging.
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Model averaging is a means of allowing for model uncertainty in estimation which can provide better estimates and more reliable confidence intervals than model selection. We illustrate its use via examples involving real data, discuss when it is likely to be useful, and compare the frequentist and Bayesian approaches to model averaging.
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2013
This chapter approaches methodologies of deriving averaged models able to also represent behavior of converters containing AC stages. This time, modeling is not restricted to DC variables and the resultant models – called generalized averaged models (GAM) – can handle averages of higher-order harmonics.
Seddik Bacha +2 more
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This chapter approaches methodologies of deriving averaged models able to also represent behavior of converters containing AC stages. This time, modeling is not restricted to DC variables and the resultant models – called generalized averaged models (GAM) – can handle averages of higher-order harmonics.
Seddik Bacha +2 more
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Model-Averaged Profile Likelihood Intervals
Journal of Agricultural, Biological, and Environmental Statistics, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fletcher, David, Turek, Daniel
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Model averaging inconcentration–QT analyses
Pharmaceutical Statistics, 2016This article describes how a frequentist model averaging approach can be used for concentration–QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite ...
Bernard, Sébastien +4 more
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2018
We provide an overview of frequentist model averaging. For point estimation, we consider different methods for selecting the model weights, including those based on AIC, bagging, weighted AIC, stacking and focussed methods. For interval estimation, we consider Wald, MATA and percentile-bootstrap intervals. Use of the methods are illustrated by examples
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We provide an overview of frequentist model averaging. For point estimation, we consider different methods for selecting the model weights, including those based on AIC, bagging, weighted AIC, stacking and focussed methods. For interval estimation, we consider Wald, MATA and percentile-bootstrap intervals. Use of the methods are illustrated by examples
openaire +1 more source