Results 11 to 20 of about 1,244,223 (283)
Macrostate data clustering [PDF]
Physical Review E, 2003 We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system.
Korenblum, Daniel, Shalloway, David
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Entropy, 2023 Multilevel semicontinuous data occur frequently in medical, environmental, insurance and financial studies. Such data are often measured with covariates at different levels; however, these data have traditionally been modelled with covariate-independent ...
Renjun Ma +3 more
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Frontiers in Pharmacology, 2023 Purpose: Surgeon and hospital-related features, such as volume, can be associated with treatment choices and outcomes. Accounting for these covariates with propensity score (PS) analysis can be challenging due to the clustered nature of the data.
Mike Du +5 more
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Stats, 2022 Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a ...
Sandipan Dutta
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High-dimensional data clustering [PDF]
Computational Statistics & Data Analysis, 2007 Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually live in different low-dimensional subspaces hidden in the original space.
Bouveyron, Charles +2 more
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R2MLwiN: A Package to Run MLwiN from within R
Journal of Statistical Software, 2016 R2MLwiN is a new package designed to run the multilevel modeling software program MLwiN from within the R environment. It allows for a large range of models to be specified which take account of a multilevel structure, including continuous, binary ...
Zhengzheng Zhang +4 more
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A Flexible Mixed Model for Clustered Count Data
Stats, 2022 Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however ...
Darcy Steeg Morris, Kimberly F. Sellers
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Dimensionality reduction of clustered data sets [PDF]
, 2008 We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant ...
Sanguinetti, G.
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Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R
Journal of Statistical Software, 2020 Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, and other social sciences. They are employed to adjust the inference following estimation of
Achim Zeileis +2 more
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Change point detection for clustered expression data
BMC Genomics, 2022 Background To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data.
Miriam Sieg +3 more
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