Results 281 to 290 of about 1,397,164 (333)

Reduced-rank growth curve models

Journal of Statistical Planning and Inference, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Reinsel, Gregory C., Velu, Raja P.
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Poisson reduced-rank models with sparse loadings

Journal of the Korean Statistical Society, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Eun Ryung, Park, Seyoung
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Tests of Rank in Reduced Rank Regression Models

Journal of Business & Economic Statistics, 2003
There has recently been renewed research interest in the development of tests of the rank of a matrix. This article evaluates the performance of some asymptotic tests of rank determination in reduced rank regression models together with bootstrapped versions through simulation experiments.
Gonzalo Camba-Mendez, George Kapetanios, Richard J Smith, Martin R Weale   +3 more
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The APT Model as Reduced-Rank Regression

Journal of Business & Economic Statistics, 1996
Integrating the two steps of an arbitrage pricing theory (APT) model leads to a reduced-rank regression (RRR) model. So the results on RRR can be used to estimate APT models, making estimation very simple. We give a succinct derivation of estimation of RRR, derive the asymptotic variance of RRR estimators for a general case, and discuss how undersized ...
Bekker, P.A., Dobbelstein, P., Wansbeek, T.J.   +2 more
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Reduced-Rank Regression Model

1998
The classical multivariate regression model presented in Chapter 1, as noted before, does not make direct use of the fact that the response variables are likely to be correlated. A more serious practical concern is that even for a moderate number of variables whose interrelationships are to be investigated, the number of parameters in the regression ...
Gregory C. Reinsel, Raja P. Velu
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Reduced-Rank Models for Nutrition Knowledge Assessment

Biometrics, 1998
Summary: The dependence of multiple interrelated responses on a set of covariates can be efficiently and parsimoniously modeled using reduced-rank methods. Two forms of reduced-rank models are possible depending on assumptions about response error correlations.
Variyam, Jayachandran N., Blaylock, James, Smallwood, David   +2 more
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Reduced Rank Models for Multiple Time Series

Biometrika, 1986
By analogy with the multivariate reduced rank regression model: \(Y_ t=ABX_ t+\epsilon_ t\), where A and B are \(m\times r\) and \(r\times n\) matrices respectively, the authors investigate reduced rank models for multiple time series \[ Y_ t=A(L)B(L)Y_{t-1}+\epsilon_ t \] where L denotes the lag operator, A and B are \(m\times r\) and \(r\times n ...
Velu, Raja P., Reinsel, Gregory C., Wichern, Dean W.   +2 more
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A Bilinear Reduced Rank Model

2020
This article considers a bilinear model that includes two different latent effects. The first effect has a direct influence on the response variable, whereas the second latent effect is assumed to first influence other latent variables, which in turn affect the response variable.
Chengcheng Hao, Feng Li, Dietrich von Rosen   +2 more
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Reduced rank regression in cointegrated models

Journal of Econometrics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Reduced-Rank Modeling for High-Dimensional Model-Based Clustering

Journal of Computational Mathematics, 2018
Model-based clustering is popularly used in statistical literature, which often models the data with a Gaussian mixture model. As a consequence, it requires estimation of a large amount of parameters, especially when the data dimension is relatively large.
Yang, Lei, Wang, Junhui, Ma, Shiqian
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