Special section on multidisciplinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come?
The use of metamodeling techniques in the design and analysis of computer experiments
has progressed remarkably in the past 25 years, but how far has the field really come? This …
has progressed remarkably in the past 25 years, but how far has the field really come? This …
Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come-or not
The use of metamodeling techniques in the design and analysis of computer experiments
has progressed remarkably in the past two decades, but how far have we really come? This …
has progressed remarkably in the past two decades, but how far have we really come? This …
A primer on PAC-Bayesian learning
B Guedj - arXiv preprint arXiv:1901.05353, 2019 - arxiv.org
Generalised Bayesian learning algorithms are increasingly popular in machine learning,
due to their PAC generalisation properties and flexibility. The present paper aims at …
due to their PAC generalisation properties and flexibility. The present paper aims at …
Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation
Y Yang - Biometrika, 2005 - academic.oup.com
A traditional approach to statistical inference is to identify the true or best model first with little
or no consideration of the specific goal of inference in the model identification stage. Can the …
or no consideration of the specific goal of inference in the model identification stage. Can the …
Adaptive regression by mixing
Y Yang - Journal of the American Statistical Association, 2001 - Taylor & Francis
Adaptation over different procedures is of practical importance. Different procedures perform
well under different conditions. In many practical situations, it is rather hard to assess which …
well under different conditions. In many practical situations, it is rather hard to assess which …
Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship
HG Acquah - 2010 - ir.ucc.edu.gh
Information criteria provide an attractive basis for model selection. However, little is
understood about their relative performance in asymmetric price transmission modelling …
understood about their relative performance in asymmetric price transmission modelling …
Combining time series models for forecasting
Statistical models (eg, ARIMA models) have commonly been used in time series data
analysis and forecasting. Typically, one model is selected based on a selection criterion (eg …
analysis and forecasting. Typically, one model is selected based on a selection criterion (eg …
Optimal weight choice for frequentist model average estimators
There has been increasing interest recently in model averaging within the frequentist
paradigm. The main benefit of model averaging over model selection is that it incorporates …
paradigm. The main benefit of model averaging over model selection is that it incorporates …
Combining forecasting procedures: some theoretical results
Y Yang - Econometric Theory, 2004 - cambridge.org
We study some methods of combining procedures for forecasting a continuous random
variable. Statistical risk bounds under the square error loss are obtained under distributional …
variable. Statistical risk bounds under the square error loss are obtained under distributional …
Information theory and mixing least-squares regressions
For Gaussian regression, we develop and analyze methods for combining estimators from
various models. For squared-error loss, an unbiased estimator of the risk of the mixture of …
various models. For squared-error loss, an unbiased estimator of the risk of the mixture of …