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15,052
Multivariate adaptive regression splines
 The Annals of Statistics
, 1991
"... A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automaticall ..."
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Cited by 700 (2 self)
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A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations
Models for Multivariate Data Analysis
"... . This paper reviews some models for exploring multivariate data. If a fixed effect model is used to define a linear Principal Components Analysis (PCA), then risk functions can be defined and issues of metric and dimension optimality addressed. The model is then adapted to define a functional PCA w ..."
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Cited by 3 (0 self)
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. This paper reviews some models for exploring multivariate data. If a fixed effect model is used to define a linear Principal Components Analysis (PCA), then risk functions can be defined and issues of metric and dimension optimality addressed. The model is then adapted to define a functional PCA
multivariate data analysis
, 1992
"... Cokriging and kriging are compared in the case when all variables are available at the same sample locations. The advantage of cokriging over kriging is that it ensures the coherence between an estimation of a sum and the separate estimation of each of its terms. To spare modeling effort it is inte ..."
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it is interesting to know in which situation the kriging of a variable is equivalent o its cokriging with respect to a set of auxiliary variables (autokrigeability). In regionalized multivariate data analysis (MDA) it is important to know whether a whole set of variables is autokrigeable (intrinsically
categorical multivariate data
"... Abstract: This article investigates unsupervised classification techniques for categorical multivariate data. The study employs multivariate multinomial mixture modeling, which is a type of model particularly applicable to multilocus genotypic data. A model selection procedure is used to simultaneou ..."
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Abstract: This article investigates unsupervised classification techniques for categorical multivariate data. The study employs multivariate multinomial mixture modeling, which is a type of model particularly applicable to multilocus genotypic data. A model selection procedure is used
Dynamic Conditional Correlation: A simple class of multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.
 Journal of Business & Economic Statistics
, 2002
"... Abstract Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models ..."
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Cited by 711 (17 self)
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Abstract Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models
Survey on Independent Component Analysis
 NEURAL COMPUTING SURVEYS
, 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 2309 (104 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation
PROPAGATION OF OUTLIERS IN MULTIVARIATE DATA
, 903
"... We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that ..."
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Cited by 4 (0 self)
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We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error
Algorithms for Nonnegative Matrix Factorization
 In NIPS
, 2001
"... Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
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Cited by 1246 (5 self)
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Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown
Distribution Free Decomposition of Multivariate Data
 Pattern Analysis and Applications
, 1998
"... We present a practical approach to nonparametric cluster analysis of large data sets. The number of clusters and the cluster centers are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries are delineat ..."
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Cited by 73 (16 self)
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We present a practical approach to nonparametric cluster analysis of large data sets. The number of clusters and the cluster centers are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries
Continuous Multivariate Data
, 2003
"... We present a semiautomatic method of outlier detection for continuous, multivariate survey data. In large datasets, outliers may be dicult to nd using informal inspection and graphical displays, particularly when there are missing values. Our method relies on an explicit probability model for the ..."
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We present a semiautomatic method of outlier detection for continuous, multivariate survey data. In large datasets, outliers may be dicult to nd using informal inspection and graphical displays, particularly when there are missing values. Our method relies on an explicit probability model
Results 1  10
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15,052