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Mixtures of Gaussian copula factor analyzers for clustering high dimensional data
Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering....
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Principal weighted logistic regression for sufficient dimension reduction in binary classification
Sufficient dimension reduction (SDR) is a popular supervised machine learning technique that reduces the predictor dimension and facilitates...
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Similar Coefficient of Cluster for Discrete Elements
This article proposes a new concept called Cluster Similar Coefficient (CSC) for discrete elements. CSC is not only used as a criterion to build...
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A Density-Sensitive Hierarchical Clustering Method
We define a hierarchical clustering method: α -unchaining single linkage or SL ( α ). The input of this algorithm is a finite space with a distance...
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Discussion of “concentration for (regularized) empirical risk minimization” by Sara van de Geer and Martin Wainwright
Sara van de Geer and Martin Wainwright combine astute convexity arguments and concentration inequalities for suprema of empirical processes to...
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High-Dimensional Quadratic Classifiers in Non-sparse Settings
In this paper, we consider high-dimensional quadratic classifiers in non-sparse settings. The quadratic classifiers proposed in this paper draw...
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Accuracy of regularized D-rule for binary classification
We consider a regularized D-classification rule for high dimensional binary classification, which adapts the linear shrinkage estimator of a...
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Bayesian Discriminant Analysis Using a High Dimensional Predictor
We consider the problem of Bayesian discriminant analysis using a high dimensional predictor. In this setting, the underlying precision matrices can...
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Uniform distribution width estimation from data observed with Laplace additive error
A one-dimensional problem of a uniform distribution width estimation from data observed with a Laplace additive error is analyzed. The error variance...
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A Measure of Downside Risk in Multivariate Setup with Application in Measuring Financial Stress
Financial Stress Indicator (FSI) combines indicators from different segments of the financial market into a unified measure, which indicates the...
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Decision boundaries for mixtures of regressions
The analysis of the decision boundaries plays an important role in understanding the characteristics of a classifier in the framework of model-based...
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Finding standard dental arch forms from a nationwide standard occlusion study using a Gaussian functional mixture model
Orthodontists are interested in finding a set of standard arch forms for clinical orthodontic practice. In this paper, we propose a functional...
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Optimal Classification Policy and Comparisons for Highly Reliable Products
In the current competitive marketplace, manufacturers need to classify products in a short period of time, according to market demand. Hence, it is a...
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Optimal classifier for multivariate rectangle-screened normal data classification
This paper discusses the classification procedures which make provision for the case where the interest of an investigator is to classify a...
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Some properties of generalized fused lasso and its applications to high dimensional data
Identifying homogeneous subgroups of variables can be challenging in high dimensional data analysis with highly correlated predictors. The...
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A Spatial Scan Statistic on Trends
Spatial scan statistics have been widely researched for detecting geographic clusters of heterogeneous rates. This article explores a relevant field...
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Global testing method for clustering means in ANOVA
For the comparison of treatment means in the analysis of variance, it is reasonable to partition the treatments into disjoint groups such that...
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Supervised classification of diffusion paths
Let X = ( X t ) t ∈[0,1] be a stochastic process with label Y ∈ {0, 1}.We assume that X is some Brownian diffusion when Y = 0, while X is another...
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A modified area under the ROC curve and its application to marker selection and classification
The area under the ROC curve (AUC) can be interpreted as the probability that the classification scores of a diseased subject is larger than that of...
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High-dimensional AICs for selection of variables in discriminant analysis
This paper is concerned with high-dimensional modifications of Akaike information criterion (AIC) for selection of variables in discriminant...