Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error [PDF]
Nonlinear Processes in Geophysics, 2018 The ensemble Kalman filter and its variants have shown to be robust for data assimilation in high dimensional geophysical models, with localization, using ensembles of extremely small size relative to the model dimension.
C. Grudzien, A. Carrassi, M. Bocquet
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A Poisson reduced-rank regression model for association mapping in sequencing data [PDF]
BMC Bioinformatics, 2022 Background Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type,
Tiana Fitzgerald +2 more
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Reduced rank proportional hazards model for competing risks [PDF]
Biostatistics, 2005 Competing events concerning individual subjects are of interest in many medical studies. For example, leukemia-free patients surviving a bone marrow transplant are at risk of developing acute or chronic graft-versus-host disease, or they might develop infections.
Marta Fiocco +2 more
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PT $$ \mathcal{P}\mathcal{T} $$ deformation of angular Calogero models [PDF]
Journal of High Energy Physics, 2017 The rational Calogero model based on an arbitrary rank-n Coxeter root system is spherically reduced to a superintegrable angular model of a particle moving on S n−1 subject to a very particular potential singular at the reflection hyperplanes.
Francisco Correa, Olaf Lechtenfeld
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Sparse reduced-rank regression for imaging genetics studies: models and applications [PDF]
, 2012 We present a novel statistical technique; the sparse reduced rank regression (sRRR) model which is a strategy for multivariate modelling of high-dimensional imaging responses and genetic predictors.
Maria Vounou
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Complex Reduced Rank Models for Seasonally Cointegrated Time Series [PDF]
Oxford Bulletin of Economics and Statistics, 2000 This paper introduces a new representation for seasonally cointegrated variables, namely the complex error correction model, which allows statistical inference to be performed by reduced rank regression. The suggested estimators and tests statistics are asymptotically equivalent to their maximum likelihood counterparts.
Gianluca Cubadda
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Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution [PDF]
, 2014 There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of ...
Casey Olives +5 more
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A comparison of assimilation results from the ensemble Kalman Filter and a reduced-rank extended Kalman Filter [PDF]
Nonlinear Processes in Geophysics, 2003 The goal of this study is to compare the performances of the ensemble Kalman filter and a reduced-rank extended Kalman filter when applied to different dynamic regimes.
X. Zang, P. Malanotte-Rizzoli
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Small area estimation using reduced rank regression models [PDF]
Communications in Statistics - Theory and Methods, 2019 Small area estimation techniques have got a lot of attention during the last decades due to their important applications in survey studies.
Tatjana von Rosen, Dietrich von Rosen
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Reduce-Rank Matrix Integer-Valued Autoregressive Model [PDF]
Integer-valued time series are widely present in many fields, such as finance, economics, disease transmission, and traffic flow. With data dimensions surging, the traditional multivariate generalized integer autoregressive (MGINAR) model faces parameter overload, poor interpretability, and structural information loss.
Kaiyan Cui, T.-M Guo, Suping Wang
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