Search results for "Muut"
showing 10 items of 1534 documents
Fast and universal estimation of latent variable models using extended variational approximations
2022
AbstractGeneralized linear latent variable models (GLLVMs) are a class of methods for analyzing multi-response data which has gained considerable popularity in recent years, e.g., in the analysis of multivariate abundance data in ecology. One of the main features of GLLVMs is their capacity to handle a variety of responses types, such as (overdispersed) counts, binomial and (semi-)continuous responses, and proportions data. On the other hand, the inclusion of unobserved latent variables poses a major computational challenge, as the resulting marginal likelihood function involves an intractable integral for non-normally distributed responses. This has spurred research into a number of approx…
Analyzing environmental‐trait interactions in ecological communities with fourth‐corner latent variable models
2021
In ecological community studies it is often of interest to study the effect of species related trait variables on abundances or presence-absences. Specifically, the interest may lay in the interactions between environmental and trait variables. An increasingly popular approach for studying such interactions is to use the so-called fourth-corner model, which explicitly posits a regression model where the mean response of each species is a function of interactions between covariate and trait predictors (among other terms). On the other hand, many of the fourth-corner models currently applied in the literature are too simplistic to properly account for variation in environmental and trait resp…
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…
Bioclimatic atlas of the terrestrial Arctic
2023
AbstractThe Arctic is the region on Earth that is warming at the fastest rate. In addition to rising means of temperature-related variables, Arctic ecosystems are affected by increasingly frequent extreme weather events causing disturbance to Arctic ecosystems. Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas > 45°N. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950–2021 in a spatial grid of 0.1 degree (~9 km) resolution. The indices are provided in three subsets…
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…
Plant trait‐environment relationships in tundra are consistent across spatial scales
2023
Patterns and processes shaping ecosystems vary across spatiotemporal scales. As plant functional traits reflect ecosystem properties, investigating their relationships with environment provides an important tool to understand and predict ecosystem structure and functioning. This is particularly important in the tundra where a changing climate may trigger severe alterations in plant communities as both summer and winter conditions are changing. Here, we investigate the relationships between key environmental drivers including summer temperature, snow persistence, topographic position and soil pH, and species height, specific leaf area (SLA) and seed mass as plant traits. The study is carried…
Maahanmuuttajat suomalaisilla työmarkkinoilla : intersektionaalisuus ja "hyvä kansalainen" työmarkkina-aseman määrittäjänä
2015
Tämä artikkeli tarkastelee maahanmuuttajavaltaisilla rakennus- ja palvelusektoreilla työskentelevien maahanmuuttajien elämäntarinoita ja heidän kokemuksiaan Suomesta. Työmarkkinoiden eriarvoisuutta ei voida täysin selittää segmentaatio- ja segregaatioteorioiden kautta. Esitämme, että intersektionaalisuuden teoria – eli analyysi risteävistä eroista - tarjoaa mahdollisuuden ymmärtää heterogeenisen maahanmuuttajajoukon työmarkkina-asemaa monisyisemmin. Aineistomme koostuu EU-kansalaisten ja EU - ja ETA – alueiden ulkopuolisten ihmisten elämäkertatarinoista. Eri asemistaan huolimatta haastateltavilla esiintyy vahvaa työn tekemisen korostamista. Tarkastelemme heidän asemoitumistaan yhtäältä Brid…