Search results for "muuttuja"
showing 10 items of 40 documents
Blind source separation for non-stationary random fields
2022
Regional data analysis is concerned with the analysis and modeling of measurements that are spatially separated by specifically accounting for typical features of such data. Namely, measurements in close proximity tend to be more similar than the ones further separated. This might hold also true for cross-dependencies when multivariate spatial data is considered. Often, scientists are interested in linear transformations of such data which are easy to interpret and might be used as dimension reduction. Recently, for that purpose spatial blind source separation (SBSS) was introduced which assumes that the observed data are formed by a linear mixture of uncorrelated, weakly stationary random …
On the usage of joint diagonalization in multivariate statistics
2022
Scatter matrices generalize the covariance matrix and are useful in many multivariate data analysis methods, including well-known principal component analysis (PCA), which is based on the diagonalization of the covariance matrix. The simultaneous diagonalization of two or more scatter matrices goes beyond PCA and is used more and more often. In this paper, we offer an overview of many methods that are based on a joint diagonalization. These methods range from the unsupervised context with invariant coordinate selection and blind source separation, which includes independent component analysis, to the supervised context with discriminant analysis and sliced inverse regression. They also enco…
Dimension reduction for time series in a blind source separation context using r
2021
Funding Information: The work of KN was supported by the CRoNoS COST Action IC1408 and the Austrian Science Fund P31881-N32. The work of ST was supported by the CRoNoS COST Action IC1408. The work of JV was supported by Academy of Finland (grant 321883). We would like to thank the anonymous reviewers for their comments which improved the paper and package considerably. Publisher Copyright: © 2021, American Statistical Association. All rights reserved. Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first red…
Test of the Latent Dimension of a Spatial Blind Source Separation Model
2024
We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated random fields containing a number of pure white noise components. We propose a test on the number of white noise components and obtain the asymptotic distribution of its statistic for a general domain. We also demonstrate how computations can be facilitated in the case of gridded observation locations. Based on this test, we obtain a consistent estimator of the true dimension. Simulation studies and an environmental application in the Supplemental Material demonstrate that our test is at least comparable to and often outperforms bootstrap-bas…
Birth weight, adult weight, and cardiovascular biomarkers : Evidence from the Cardiovascular Young Finns Study
2021
This study quantifies the causal effect of birth weight on cardiovascular biomarkers in adulthood using the Cardiovascular Risk in Young Finns Study (YFS). We apply a multivariable Mendelian randomization (MVMR) method that provides a novel approach to improve inference in causal analysis based on a mediation framework. The results show that birth weight is linked to triglyceride levels (β = −0.294; 95% CI [−0.591, 0.003]) but not to low-density lipoprotein (LDL) cholesterol levels (β = 0.007; 95% CI [−0.168, 0.183]). The total effect of birth weight on triglyceride levels is partly offset by a mediation pathway linking birth weight to adult BMI (β = 0.111; 95% CI [−0.013, 0.234]). The nega…
A review of second‐order blind identification methods
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 important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modelling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source sig…
Maaston ja juoksunopeuden vaikutukset askelmuuttujiin suunnistusjuoksusuorituksessa
2014
Johdanto. Suunnistuksessa sekä fyysinen että taidollinen elementti vaikuttavat lopputulokseen. Suunnistusjuoksusuorituksessa taas edetään maastoon viitoitettua reittiä pitkin. Suunnistusjuoksu eroaa tasamaan juoksusta maaston jatkuvan vaihtelun, maastopohjan esteiden sekä korkeuserojen myötä. Nämä tekijät vaikuttavat myös suunnistusjuoksutekniikkaan. Tässä tutkimuksessa selvitettiin suunnistusjuoksun askelmuuttujien vaihtelua maastonosien sekä juoksunopeuden mukaan. Menetelmät. Tutkimukseen osallistui 10 miessuunnistajaa. Tutkimukset suoritettiin kahtena päivänä siten, että ensimmäinen päivä sisälsi antropometrisia mittauksia sekä suoran hapenottokykytestin juoksumatolla (VO2max -testi). Yh…
An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods
2021
Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phas…
Extracting Conditionally Heteroskedastic Components using Independent Component Analysis
2020
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 coef…
Long-term impacts of increased timber harvests on ecosystem services and biodiversity : A scenario study based on national forest inventory data
2020
The transition to a climate-neutral economy is expected to increase future timber demands and endanger the multifunctionality of forests. National scenario analyses are needed to determine long-term forest management impacts and support forest policy making in defining guidelines for the sustainable provision of forests’ ecosystem services and biodiversity (ESB). Using national forestry inventory data, the forest management model MASSIMO and a model to estimate harvesting costs, we simulated forest development in Switzerland under five politically relevant timber harvesting scenarios until 2106 (business as usual and four increased timber mobilisation scenarios). Model results were analysed…