Penalized Reduced Rank Regression for Multi‐Outcome Survival Data Supports a Common Metabolic Risk Score for Age‐Related Diseases
Sign Up to like & getrecommendations! Published in 2024 at "Statistics in Medicine"
DOI: 10.1002/sim.70156
Abstract: The increasing availability of multi-outcome data in health research presents new opportunities for understanding complex health processes, such as ageing. Ageing is a multifaceted process, encompassing both lifespan and healthspan, as well as the onset… read more here.
Keywords: age; reduced rank; related diseases; age related ... See more keywords
Non-supersymmetric non-tachyonic heterotic vacua with reduced rank in various dimensions
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DOI: 10.1007/jhep10(2024)216
Abstract: We construct rigid non-supersymmetric heterotic vacua with reduced rank and no tachyons in six and four dimensions. These configurations are based on asymmetric orbifold compactifications which do not admit neutral deformation moduli and represent, to… read more here.
Keywords: non supersymmetric; heterotic vacua; reduced rank; vacua reduced ... See more keywords
Dietary patterns derived by reduced rank regression, macronutrients as response variables, and variation by economic status: NHANES 1999–2018
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DOI: 10.1007/s00394-024-03501-z
Abstract: Macronutrient intakes vary across people and economic status, leading to a disparity in diet-related metabolic diseases. This study aimed to provide insight into this by: (1) identifying dietary patterns in adults using reduced rank regression… read more here.
Keywords: response variables; economic status; dietary patterns; reduced rank ... See more keywords
Reduced-rank multi-label classification
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DOI: 10.1007/s11222-015-9615-0
Abstract: Multi-label classification is a natural generalization of the classical binary classification for classifying multiple class labels. It differs from multi-class classification in that the multiple class labels are not exclusive. The key challenge is to… read more here.
Keywords: reduced rank; label classification; classification; multi label ... See more keywords
Hilbert space methods for reduced-rank Gaussian process regression
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DOI: 10.1007/s11222-019-09886-w
Abstract: This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in… read more here.
Keywords: reduced rank; gaussian process; covariance function;
Robust reduced-rank modeling via rank regression
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Statistical Planning and Inference"
DOI: 10.1016/j.jspi.2016.08.009
Abstract: Abstract There are many applications in which several response variables are predicted with a common set of predictors. To take into account the possible correlations among the responses, estimators with restricted rank were introduced. However,… read more here.
Keywords: reduced rank; regression; via rank; rank regression ... See more keywords
Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications
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DOI: 10.1016/j.neuroimage.2016.08.027
Abstract: ABSTRACT We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging‐genetic studies to identify… read more here.
Keywords: reduced rank; high dimensional; regression; tensor ... See more keywords
Effective hardware implementation of Volterra filters based on reduced‐rank approaches
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DOI: 10.1049/el.2017.4776
Abstract: The focus of this Letter is on the development of a new effective approach for the hardware implementation of Volterra filters. The proposed approach is based on exploiting the different significance levels of the branches… read more here.
Keywords: reduced rank; implementation; implementation volterra; volterra filters ... See more keywords
Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models
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DOI: 10.1080/02331888.2018.1467420
Abstract: ABSTRACT Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are… read more here.
Keywords: reduced rank; rank multivariate; multivariate generalized; theory ... See more keywords
Estimation bias and bias correction in reduced rank autoregressions
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DOI: 10.1080/07474938.2017.1308065
Abstract: ABSTRACT This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias… read more here.
Keywords: reduced rank; bias bias; correction reduced; bias correction ... See more keywords
Multiple augmented reduced rank regression for pan-cancer analysis.
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DOI: 10.1093/biomtc/ujad002
Abstract: Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose… read more here.
Keywords: cancer; reduced rank; augmented reduced; rank regression ... See more keywords