Acta Et Commentationes Universitatis Tartuensis de Mathematica PDF Download
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Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 132
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Book Description
Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 132
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 554
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Book Description
Author: Tartu Riiklik Ülikool
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 1358
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Book Description
Author: Tartu Riiklik Ülikool
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 436
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Book Description
Author: National Library of Medicine (U.S.)
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 1516
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Book Description
A keyword listing of serial titles currently received by the National Library of Medicine.
Author: Curtis R. Vogel
Publisher: SIAM
ISBN: 0898715504
Category : Mathematics
Languages : en
Pages : 195
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Book Description
Provides a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems.
Author:
Publisher:
ISBN:
Category : Humanities
Languages : et
Pages : 1110
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Book Description
Author: Simo Puntanen
Publisher: Springer Science & Business Media
ISBN: 3642104738
Category : Mathematics
Languages : en
Pages : 504
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Book Description
In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple “tricks” which simplify and clarify the treatment of a problem—both for the student and for the professor. Of course, the concept of a trick is not uniquely defined—by a trick we simply mean here a useful important handy result. In this book we collect together our Top Twenty favourite matrix tricks for linear statistical models.
Author: American Geographical Society of New York
Publisher:
ISBN:
Category : Geography
Languages : en
Pages : 862
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Author: Joseph Nzabanita
Publisher: Linköping University Electronic Press
ISBN: 9175190702
Category : Matrices
Languages : en
Pages : 51
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Book Description
This thesis focuses on the problem of estimating parameters in bilinear and trilinear regression models in which random errors are normally distributed. In these models the covariance matrix has a Kronecker product structure and some factor matrices may be linearly structured. The interest of considering various structures for the covariance matrices in different statistical models is partly driven by the idea that altering the covariance structure of a parametric model alters the variances of the model’s estimated mean parameters. Firstly, the extended growth curve model with a linearly structured covariance matrix is considered. The main theme is to find explicit estimators for the mean and for the linearly structured covariance matrix. We show how to decompose the residual space, the orthogonal complement to the mean space, into appropriate orthogonal subspaces and how to derive explicit estimators of the covariance matrix from the sum of squared residuals obtained by projecting observations on those subspaces. Also an explicit estimator of the mean is derived and some properties of the proposed estimators are studied. Secondly, we study a bilinear regression model with matrix normally distributed random errors. For those models, the dispersion matrix follows a Kronecker product structure and it can be used, for example, to model data with spatio-temporal relationships. The aim is to estimate the parameters of the model when, in addition, one covariance matrix is assumed to be linearly structured. On the basis of n independent observations from a matrix normal distribution, estimating equations, a flip-flop relation, are established. At last, the models based on normally distributed random third order tensors are studied. These models are useful in analyzing 3-dimensional data arrays. In some studies the analysis is done using the tensor normal model, where the focus is on the estimation of the variance-covariance matrix which has a Kronecker structure. Little attention is paid to the structure of the mean, however, there is a potential to improve the analysis by assuming a structured mean. We formally introduce a 2-fold growth curve model by assuming a trilinear structure for the mean in the tensor normal model and propose an estimation algorithm for parameters. Also some extensions are discussed.
