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Shaohua Wu
Researcher at Honeywell
Publications - 7
Citations - 259
Shaohua Wu is an academic researcher from Honeywell. The author has contributed to research in topics: Mean squared error & Estimation theory. The author has an hindex of 6, co-authored 7 publications receiving 223 citations. Previous affiliations of Shaohua Wu include Queen's University & Honeywell Aerospace.
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The use of simplified or misspecified models : Linear case
TL;DR: In this paper, the authors summarize extensive quantitative and qualitative results in the literature concerned with using simplified or misspecified models and develop a practical strategy to help modellers decide whether a simplified model should be used, and point out the difficulty in making such a decision.
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Selection of optimal parameter set using estimability analysis and MSE-based model-selection criterion
TL;DR: In this article, a mean squared error (MSE)-based model selection criterion is used to determine the optimal number of parameters to estimate from the ranked parameter list, so that the most reliable model predictions can be obtained.
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Mean-Squared-Error Methods for Selecting Optimal Parameter Subsets for Estimation
TL;DR: In this paper, an orthogonalization algorithm combined with a mean squared error (MSE) based selection criterion has been used to rank parameters from most to least estimable and to determine the parameter subset that should be estimated to obtain the best predictions.
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Selection of simplified models: ii. development of a model selection criterion based on mean squared error
TL;DR: In this article, the authors proposed a new criterion to help modellers select the best simplified model with the lowest expected mean squared error (EME) and compared it with the effectiveness of Bayesian Information Criterion (BIC).
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Selection of simplified models: I. Analysis of model‐selection criteria using mean‐squared error
TL;DR: Mean-squared error (MSE) is used to analyse nine commonly used model selection criteria (MSC) for their performance when selecting simplified models (SMs) as discussed by the authors.