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• A Review of Generalized Linear Latent Variable Models and Related Computational Approaches 

  Korhonen, Pekka; Nordhausen, Klaus; Taskinen, Sara (Wiley, 2024)

  Generalized linear latent variable models (GLLVMs) have become mainstream models in this analysis of correlated, m-dimensional data. GLLVMs can be seen as a reduced-rank version of generalized linear mixed models (GLMMs) ...

• Modeling Forest Tree Data Using Sequential Spatial Point Processes 

  Yazigi, Adil; Penttinen, Antti; Ylitalo, Anna-Kaisa; Maltamo, Matti; Packalen, Petteri; Mehtätalo, Lauri (Springer, 2022)

  The spatial structure of a forest stand is typically modeled by spatial point process models. Motivated by aerial forest inventories and forest dynamics in general, we propose a sequential spatial approach for modeling ...

• gllvm : Fast analysis of multivariate abundance data with generalized linear latent variable models in R 

  Niku, Jenni; Hui, Francis K.C.; Taskinen, Sara; Warton, David I. (Wiley, 2019)

  1.There has been rapid development in tools for multivariate analysis based on fully specified statistical models or “joint models”. One approach attracting a lot of attention is generalized linear latent variable models ...

• Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm 

  Liu, Jia; Gasbarra, Dario; Railavo, Juha (Elsevier BV, 2016)

  Diffusion tensor imaging (DTI) is widely used to characterize, in vivo, the white matter of the central nerve system (CNS). This biological tissue contains much anatomic, structural and orientational information of fibers ...

• Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R 

  Helske, Jouni (Elsevier BV, 2022)

  The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as ...