Issue Information
Optimization and testing in linear non‐Gaussian component analysis
Independent component analysis (ICA) decomposes multivariate data into mutually independent components (ICs). The ICA model is subject to a constraint that at most one of these components is Gaussian, which is required for model identifiability. Linear ...
On relationship formation in heterogeneous information networks: An inferring method based on multilabel learning
This paper studies how relationships form in heterogeneous information networks (HINs). The objective is not only to predict relationships in a given HIN more accurately but also to discover the interdependency between different type of relationships. A ...
Pruning variable selection ensembles
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering‐based selective ensemble learning strategy is designed in ...
Spatial modeling of brain connectivity data via latent distance models with nodes clustering
Brain network data—measuring structural interconnections among brain regions of interest—are increasingly collected for multiple individuals. Moreover, recent analyses provide additional information on the brain regions under study. These predictors ...
Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments†
With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial data sets. This has generated substantial interest over the last decade, already too vast to ...
Assessing topic model relevance: Evaluation and informative priors
Latent Dirichlet allocation (LDA) models trained without stopword removal often produce topics with high posterior probabilities on uninformative words, obscuring the underlying corpus content. Even when canonical stopwords are manually removed, ...
TiK‐means: Transformation‐infused K‐means clustering for skewed groups
The K‐means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK‐means and contributes a K‐means‐type algorithm that assigns observations to groups while estimating their skewness‐transformation parameters. ...
Two‐sample homogeneity testing: A procedure based on comparing distributions of interpoint distances
A new test statistic using interpoint distances is proposed to address the two‐sample problem for multivariate populations. The test statistic compares univariate distributions of within and between samples pairwise distances using a Cramér‐von ...
