Extracting conditionally heteroskedastic components using independent component analysis
Miettinen, J., Matilainen, M., Nordhausen, K., & Taskinen, S. (2020). Extracting conditionally heteroskedastic components using independent component analysis. Journal of Time Series Analysis, 41(2), 293-311. https://doi.org/10.1111/jtsa.12505
Julkaistu sarjassa
Journal of Time Series AnalysisPäivämäärä
2020Tekijänoikeudet
© 2019 the Author(s)
In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA‐GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coefficient. As it is often of interest to identify all those components which exhibit stochastic volatility features we suggest a test statistic for this problem. We also show that a slightly modified version of the principal volatility component analysis can be seen as an ICA method. Finally, we apply the estimators in analysing a data set which consists of time series of exchange rates of seven currencies to US dollar. Supporting information including proofs of the theorems is available online.
...
Julkaisija
Wiley-BlackwellISSN Hae Julkaisufoorumista
0143-9782Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/32834454
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
The work of K.N. was supported by the Austrian Science Fund (FWF) Grant number P31881‐N32.Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Asymptotic and bootstrap tests for subspace dimension
Nordhausen, Klaus; Oja, Hannu; Tyler, David E. (Elsevier, 2022)Many linear dimension reduction methods proposed in the literature can be formulated using an appropriate pair of scatter matrices. The eigen-decomposition of one scatter matrix with respect to another is then often used ... -
Multivariate Independent Component Analysis Identifies Patients in Newborn Screening Equally to Adjusted Reference Ranges
Kouři,l Štěpán; de Sousa, Julie; Fačevicová Kamila; Gardlo, Alžběta; Muehlmann, Christoph; Nordhausen, Klaus; Friedecký, David; Adam, Tomáš (MDPI, 2023)Newborn screening (NBS) of inborn errors of metabolism (IEMs) is based on the reference ranges established on a healthy newborn population using quantile statistics of molar concentrations of biomarkers and their ratios. ... -
Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
Markkanen, Merri-Lotta (2023)Lääketieteen parissa perinteiset kyselytutkimukset ovat yhä suosittuja, jonka myötä myös järjestysasteikollisten muuttujien analyysia suoritetaan paljon. Modernin teknologian kehittyminen näkyy kuitenkin myös tällä ... -
Poverty, inequality and the Finnish 1860s famine
Voutilainen, Miikka (University of Jyväskylä, 2016) -
A review of second‐order blind identification methods
Pan, Yan; Matilainen, Markus; Taskinen, Sara; Nordhausen, Klaus (John Wiley & Sons, 2022)Second order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.