Results 291 to 300 of about 1,397,164 (333)

Reduced-rank vector generalized linear models

Statistical Modelling, 2003
Reduced-rank regression is a method with great potential for dimension reduction but has found few applications in applied statistics. To address this, reduced-rank regression is proposed for the class of vector generalized linear models (VGLMs), which is very large. The resulting class, which we call reduced-rank VGLMs (RR-VGLMs), enables the benefits
Yee, Thomas W., Hastie, Trevor J.
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Seemingly unrelated reduced-rank regression model

Journal of Statistical Planning and Inference, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Velu, Raja, Richards, Joseph
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Regularized reduced rank growth curve models

Computational Statistics & Data Analysis, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Takane, Yoshio, Jung, Kwanghee, Hwang, Heungsun   +2 more
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Rank reducible varying coefficient model

Journal of Statistical Planning and Inference, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A reduced-rank Eigenbasis MIMO channel model

2008 Wireless Telecomunications Symposium, 2008
In designing optimal signaling strategies, the need for accurate yet simple channel models is ever-present. In this paper, we develop and investigate a reduced form of an analytical channel model using measured MIMO channel matrices. We investigate the use of this reduced form on predicting MIMO system performance for a square M-QAM closed loop MIMO ...
Leslie Wood, William S. Hodgkiss
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Reduced-Rank and Nonstationary Co-Integrated Models

1993
In this chapter we present some additional topics concerning the modeling of vector time series. These include the examination of models which incorporate special structure in their parameterization, in particular, the nested reduced-rank models, which attempt to cope with the problem of the high dimensionality of the parameters in the vector models ...
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Filter model of reduced-rank noise reduction

1996
The key step in reduced-rank noise reduction algorithms is to approximate a matrix by another one with lower rank, typically by truncating a singular value decomposition (SVD). We give an explicit and closed-form derivation of the filter properties of the rank reduction operation and interpret this operation in the frequency domain by showing that the ...
Hansen, Per Christian, Jensen, Søren Holdt   +1 more
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Reduced Rank Models with Two Sets of Regressors

Applied Statistics, 1991
Summary: Interest has been growing in the use and extensions of multivariate reduced rank regression procedures in applied research and data modelling. This paper considers an extension of the model proposed by Anderson. Asymptotic theory and an iterative computational procedure for the relevant estimators of the extended model are briefly discussed ...
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Reduced-rank hazard regression for modelling non-proportional hazards

Statistics in Medicine, 2006
The Cox proportional hazards model is the most common method to analyse survival data. However, the proportional hazards assumption might not hold. The natural extension of the Cox model is to introduce time-varying effects of the covariates. For some covariates such as (surgical)treatment non-proportionality could be expected beforehand.
Perperoglou, Aris, Cessie, Saskia le, van Houwelingen, Hans C   +2 more
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Multiple Time Series Modeling With Reduced Ranks

1998
There has been growing interest in multiple time series modeling, particularly through use of vector autoregressive moving average models. The subject has found appeal and has applications in various disciplines, including engineering, physical sciences, business and economics, and the social sciences.
Gregory C. Reinsel, Raja P. Velu
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