Search Results - subject:"Functional principal component analysis"

Nonstationarity in time series of state densities

Chang, Yoosoon; Kim, Chang Sik; Park, Joon Y. - In: Journal of econometrics 192 (2016) 1, pp. 152-167

Persistent link: https://www.econbiz.de/10011617129

Modeling hourly Electricity Spot Market Prices as non stationary functional times series

Liebl, Dominik - Volkswirtschaftliche Fakultät, … - 2010

paper we introduce a robust version of functional principal component analysis for sparse data. The functional perspective …

Persistent link: https://www.econbiz.de/10008642664

Nonparametric modeling and forecasting electricity demand: an empirical study

Shang, Han Lin - Department of Econometrics and Business Statistics, … - 2010

series of curves. The forecasting methods reduce the dimensionality by applying functional principal component analysis to …

Persistent link: https://www.econbiz.de/10008725785

Nonparametric time series forecasting with dynamic updating

Shang, Han Lin; Hyndman, Rob J - Department of Econometrics and Business Statistics, … - 2009

. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical …

Persistent link: https://www.econbiz.de/10004998471

DUZGUNLESTIRILMIS FONKSIYONEL ANA BILESENLER ANALIZI ILE IMKB VERILERININ INCELENMESI

Ertas, Kadir; Keser, Istem Koymen - In: Istanbul University Econometrics and Statistics e-Journal 8 (2008) 1, pp. 1-32

In most situations, modern technological developments give rise to the cases where samples are drawn from a population of real random functions. Functional Data Analysis (FDA) is an appropriate multivariate statistical approximation since the classical multivariate methods can not be used when a...

Persistent link: https://www.econbiz.de/10005012089

Series expansion for functional sufficient dimension reduction

Lian, Heng; Li, Gaorong - In: Journal of Multivariate Analysis 124 (2014) C, pp. 150-165

Functional data are infinite-dimensional statistical objects which pose significant challenges to both theorists and practitioners. Both parametric and nonparametric regressions have received attention in the functional data analysis literature. However, the former imposes stringent constraints...

Persistent link: https://www.econbiz.de/10010737766

Functional data clustering: a survey

Jacques, Julien; Preda, Cristian - In: Advances in Data Analysis and Classification 8 (2014) 3, pp. 231-255

Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are proposed. The first group consists of methods working directly on the evaluation points of the curves. The second groups is defined by filtering methods which first approximate...

Persistent link: https://www.econbiz.de/10010949657

Principal differential analysis of the Aneurisk65 data set

Rosa, Matilde Dalla; Sangalli, Laura; Vantini, Simone - In: Advances in Data Analysis and Classification 8 (2014) 3, pp. 287-302

effect of the vessel surface, that functional principal component analysis is not able to detect. Copyright Springer … sets. In particular, we compare the results provided by principal differential analysis and by functional principal … component analysis in the dimensional reduction of three synthetic data sets, and of a real data set concerning 65 three …

Persistent link: https://www.econbiz.de/10010995273

Model-based clustering for multivariate functional data

Jacques, Julien; Preda, Cristian - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 92-106

The first model-based clustering algorithm for multivariate functional data is proposed. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal component scores, is defined and estimated...

Persistent link: https://www.econbiz.de/10010719669

Estimating mixture of Gaussian processes by kernel smoothing

Huang, Mian; Li, Runze; Wang, Hansheng; Yao, Weixin - In: Journal of business & economic statistics : JBES ; a … 32 (2014) 2, pp. 259-270

Persistent link: https://www.econbiz.de/10010488564