Remarks on a Multivariate Transformation
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This article is published in Annals of Mathematical Statistics.The article was published on 1952-09-01 and is currently open access. It has received 2735 citations till now. The article focuses on the topics: Transformation (function).read more
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On the convergence rate of maximal deviation distribution for kernel regression estimates
TL;DR: In this paper, it was shown that the distribution of the maximal deviation tends to double exponent with logarithmic rate and this rate cannot be improved, and it is shown that there is no improvement in this rate.
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Forecasting with Instabilities: an Application to DSGE Models with Financial Frictions
Roberta Cardani,Roberta Cardani,Alessia Paccagnini,Alessia Paccagnini,Stefania Villa,Stefania Villa +5 more
TL;DR: In this article, the authors assess the importance of parameter instabilities from a forecasting viewpoint in a set of medium-scale DSGE models with and without financial frictions using US real-time data.
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Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states
TL;DR: This article developed enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques.
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An adaptive directional importance sampling method for structural reliability analysis
TL;DR: In this paper, the importance sampling is merged with directional simulation and a sampling function is defined on the unit hyper sphere which samples random directions and directions are sampled around a direction that aims to the design point.
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A stochastic collocation method for large classes of mechanical problems with uncertain parameters
TL;DR: In this article, a self-contained and didactic approach to the stochastic collocation method relies on the Lagrange polynomials and the Gauss quadrature rule.