2012/11/30 by Daniel Gervini, Gervini, Daniel
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Probability and Risk Models #Stochastic processes and financial applications #stat.ME
paper · pdf · doi:10.48550/arxiv.1211.7336
arxiv created 2012/11/30 · openalex publication_date 2012/11/30 · arxiv updated 2012/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this article we present some statistical applications of the functional singular value decomposition (FSVD). This tool allows us to decompose the sample mean of a bivariate stochastic process into components that are functions of separate variables. These components are sometimes interpretable functions that summarize salient features of the data. The FSVD can be used to visually detect outliers, to estimate the mean of a stochastic process or to obtain individual smoothers of the sample surfaces. As estimators of the mean, we show by simulation that FSVD estimators are competitive with tensor-product splines in some situations.