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pro vyhledávání: '"Subedi, Sanjeena"'
Cluster-weighted factor analyzers (CWFA) are a versatile class of mixture models designed to estimate the joint distribution of a random vector that includes a response variable along with a set of explanatory variables. They are particularly valuabl
Externí odkaz:
http://arxiv.org/abs/2411.03388
A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which resulted in flexible models for clustering purposes. In particular, a class of eight parsimonious mixture models based
Externí odkaz:
http://arxiv.org/abs/2311.07762
Mixtures of shifted asymmetric Laplace distributions were introduced as a tool for model-based clustering that allowed for the direct parameterization of skewness in addition to location and scale. Following common practices, an expectation-maximizat
Externí odkaz:
http://arxiv.org/abs/2303.14211
Bi-clustering is a technique that allows for the simultaneous clustering of observations and features in a dataset. This technique is often used in bioinformatics, text mining, and time series analysis. An important advantage of biclustering algorith
Externí odkaz:
http://arxiv.org/abs/2302.03849
Publikováno v:
In Computational Statistics and Data Analysis August 2024 196
Autor:
Tu, Wangshu, Subedi, Sanjeena
The human microbiome plays an important role in human health and disease status. Next generating sequencing technologies allow for quantifying the composition of the human microbiome. Clustering these microbiome data can provide valuable information
Externí odkaz:
http://arxiv.org/abs/2101.01871
Autor:
Fang, Yuan, Subedi, Sanjeena
Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative
Externí odkaz:
http://arxiv.org/abs/2011.06682
Autor:
Tu, Wangshu, Subedi, Sanjeena
Biclustering is used for simultaneous clustering of the observations and variables when there is no group structure known \textit{a priori}. It is being increasingly used in bioinformatics, text analytics, etc. Previously, biclustering has been intro
Externí odkaz:
http://arxiv.org/abs/2009.05098
Mixtures of multivariate normal inverse Gaussian (MNIG) distributions can be used to cluster data that exhibit features such as skewness and heavy tails. However, for cluster analysis, using a traditional finite mixture model framework, either the nu
Externí odkaz:
http://arxiv.org/abs/2005.05324
Non-Gaussian mixture models are gaining increasing attention for mixture model-based clustering particularly when dealing with data that exhibit features such as skewness and heavy tails. Here, such a mixture distribution is presented, based on the m
Externí odkaz:
http://arxiv.org/abs/2005.02585