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Nested Reduced-Rank Autoregressive Models for Multiple Time Series
Journal of the American Statistical Association, 1988Abstract The nested reduced-rank autoregressive (AR) model is considered in order to simplify and provide a more detailed description of the structure of the multivariate time series and to reduce the number of parameters in the time series modeling. The multivariate AR model is Yt = Σ p j=1 Φ j Y t-j + et , where Yt is m × 1, and the structure of the ...
Sung K. Ahn, Gregory C. Reinsel
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Forecasting large datasets with reduced rank multivariate models [PDF]
, 2007 The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance with the most promising existing alternatives, namely, factor models, large scale bayesian VARs, and multivariate boosting. Specifically, we
Andrea Carriero +2 more
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Fault Detection Using Reduced Rank Linear Engine Models
Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy, 2016In diagnostic applications, data acquired from a unit in operation is often compared to predictions generated from a reference model. The equipment condition is often assessed via residual analysis, which compares the running data to model predictions. Often, the reference model may take the form of a high-fidelity, first principles physics model. Here,
Cody W. Allen +2 more
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Reduced-Rank Regression Model With Autoregressive Errors
1998The classical multivariate regression methods are based on the assumptions that (i) the regression coefficient matrix is of full rank and (ii) the error terms in the model are independent. In Chapters 2 and 3, we have presented regression models that describe the linear relationships between two or more large sets of variables with a fewer number of ...
Gregory C. Reinsel, Raja P. Velu
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Seemingly Unrelated Regressions Models With Reduced Ranks
1998The classical multivariate linear regression model discussed in Chapter 1 can be generalized by allowing the different response variables y ik to have different input or predictor variables X ik = (x i1k ,... , x ink ,)’ for different i, so that y ik - X’ ik C (i) + ∈ ik , i = 1,...
Gregory C. Reinsel, Raja P. Velu
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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The biofilm life cycle: expanding the conceptual model of biofilm formation
Nature Reviews Microbiology, 2022Karin Sauer +2 more
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