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24
Bilinear Mixed Effects Models for Dyadic Data
, 2003
"... This article discusses the use of a symmetric multiplicative interaction effect to capture certain types of thirdorder dependence patterns often present in social networks and other dyadic datasets. Such an effect, along with standard linear fixed and random effects, is incorporated into a general ..."
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This article discusses the use of a symmetric multiplicative interaction effect to capture certain types of thirdorder dependence patterns often present in social networks and other dyadic datasets. Such an effect, along with standard linear fixed and random effects, is incorporated into a generalized linear model, and a Markov chain Monte Carlo algorithm is provided for Bayesian estimation and inference. In an example analysis of international relations data, accounting for such patterns improves model fit and predictive performance.
Model averaging and dimension selection for the singular value decomposition
 Journal of the American Statistical Association
, 2007
"... Many multivariate data analysis techniques for an m × n matrix Y are related to the model Y = M+E, where Y is an m×n matrix of full rank and M is an unobserved mean matrix of rank K < (m ∧ n). Typically the rank of M is estimated in a heuristic way and then the leastsquares estimate of M is obta ..."
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Cited by 8 (1 self)
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Many multivariate data analysis techniques for an m × n matrix Y are related to the model Y = M+E, where Y is an m×n matrix of full rank and M is an unobserved mean matrix of rank K < (m ∧ n). Typically the rank of M is estimated in a heuristic way and then the leastsquares estimate of M is obtained via the singular value decomposition of Y, yielding an estimate that can have a very high variance. In this paper we suggest a modelbased alternative to the above approach by providing prior distributions and posterior estimation for the rank of M and the components of its singular value decomposition. In addition to providing more accurate inference, such an approach has the advantage of being extendable to more general dataanalysis situations, such as inference in the presence of missing data and estimation in a generalized linear modeling framework.
Robust factorization of a data matrix
 In COMPSTAT, Proceedings in Computational Statistics
, 1998
"... Abstract. In this note we show how the entries of a data matrix can be approximated by a sum of row effects, column effects and interaction terms in a robust way using a weighted L1 estimator. We discuss an algorithm to compute this fit, and show by a simulation experiment and an example that the pr ..."
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Cited by 6 (0 self)
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Abstract. In this note we show how the entries of a data matrix can be approximated by a sum of row effects, column effects and interaction terms in a robust way using a weighted L1 estimator. We discuss an algorithm to compute this fit, and show by a simulation experiment and an example that the proposed method can be a useful tool in exploring data matrices. Moreover, a robust biplot is produced as a byproduct.
Analysis of Longitudinal Metabolomics Data
"... Motivation: Metabolomics datasets are generally large and complex. Using Principal Component Analysis (PCA), a simplified view of the variation in the data is obtained. The PCAmodel can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics often a pr ..."
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Motivation: Metabolomics datasets are generally large and complex. Using Principal Component Analysis (PCA), a simplified view of the variation in the data is obtained. The PCAmodel can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics often a priori information is present about the data. Various forms of this information can be used in an unsupervised data analysis with Weighted PCA (WPCA). A WPCAmodel will give a view on the data that is different from the view obtained using PCA and it will add to the interpretation of the information in a metabolomics dataset. Results: A method is presented to translate spectra of repeated measurements into weights describing the experimental error. These weights are used in a data analysis with WPCA. The WPCAmodel will give a view on the data where the nonuniform experimental error is accounted for. Therefore the WPCA model will focus more on the natural variation in the data. Availability: Mfiles for MATLAB for the algorithm used in this research are
Modeling dependencies in international relations networks
 Political Analysis
, 2004
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Monitoring and Modeling
 the Cure Processing Properties of Resin Transfer Molding Resins, International SAMPE Symposium and Exhibition
, 1989
"... combined harvest and effort regulations: the case of the Dutch beam trawl fishery for plaice and sole in the North Sea ..."
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combined harvest and effort regulations: the case of the Dutch beam trawl fishery for plaice and sole in the North Sea
Szustalewicz A.: Some issues connected with a 3D representation of multivariate data points
 Machine Graphics & Vision
"... The presented considerations complement our former paper (B&Asz) entitled “The augmented biplot and some examples of its use ” (Machine Graphics and Vision, 4, 1995, 161–185). Now we consider some topics concerned with a 3D representation of n points–individuals and p points– variables included ..."
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The presented considerations complement our former paper (B&Asz) entitled “The augmented biplot and some examples of its use ” (Machine Graphics and Vision, 4, 1995, 161–185). Now we consider some topics concerned with a 3D representation of n points–individuals and p points– variables included in a data matrix Xn×p. The representation is displayed in the form of a spinplot called also spinner. Of course, the 3D representation exhibits the true interrelation structure between the displayed points only to some degree of accuracy. We are concerned with the display of two kinds of information: (i) the goodness of the representation of individual points, and (ii) recognizing the mutual position (the out–of–page position) of several points when viewed in a projection plane displaying the flat projection of the whole system which is spinned by performed rotations. Our considerations and needs coming from statistical data analysis problems resulted in a computer program named ASZE (A Slice and siZE displaying plot). We present 3D displays made by this program for the oat varieties data and locations of their growing and the farm production data considered formerly by B&Asz. We show on these examples what a variety of questions can be answered when constructing the spinner with appropriate enhancements. Key words: biplot, 3D plot, spinner, interdependence between variables, reduction of dimensionality 1 Introduction. What
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, 2008
"... Model averaging and dimension selection for the singular value decomposition ..."
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Model averaging and dimension selection for the singular value decomposition