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MM for penalized estimation
Penalized estimation can conduct variable selection and parameter estimation simultaneously. The general framework is to minimize a loss function...
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CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm
In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned...
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A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlap** community detection
In this paper, we focus on overlap** community detection and propose an efficient semi-orthogonal nonnegative matrix tri-factorization (semi-ONMTF)...
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A new algorithm and a discussion about visualization for logistic reduced rank regression
Logistic reduced rank regression is a useful data analysis tool when we have multiple binary response variables and a set of predictors. In this...
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Kernel density estimation by stagewise algorithm with a simple dictionary
This study proposes multivariate kernel density estimation by stagewise minimization algorithm based on U -divergence and a simple dictionary. The...
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Active-set based block coordinate descent algorithm in group LASSO for self-exciting threshold autoregressive model
Group LASSO (gLASSO) estimator has been recently proposed to estimate thresholds for the self-exciting threshold autoregressive model, and a group...
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Finite Mixture of Birnbaum–Saunders Distributions Using the k-Bumps Algorithm
Mixture models have received a great deal of attention in statistics due to the wide range of applications found in recent years. This paper...
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Speeding up the convergence of the alternating least squares algorithm using vector \(\varepsilon \) acceleration and restarting for nonlinear principal component analysis
Principal component analysis (PCA) is a widely used descriptive multivariate technique in the analysis of quantitative data. When applying PCA to...
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An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering
An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA...
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Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data
The human microbiome plays an important role in human health and disease status. Next-generating sequencing technologies allow for quantifying the...
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ROBOUT: a conditional outlier detection methodology for high-dimensional data
This paper presents a methodology, called ROBOUT, to identify outliers conditional on a high-dimensional noisy information set. In particular, ROBOUT...
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Robust variable selection for the varying index coefficient models
Recently, the statistical inference of the varying index coefficient model has been widely concerned. However, to the best of our knowledge, there...
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Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data
The article presents an algorithm for fast and error-free determination of statistics such as the arithmetic mean and variance of all contiguous...
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The MELODIC Family for Simultaneous Binary Logistic Regression in a Reduced Space
Logistic regression is a commonly used method for binary classification. Researchers often have more than a single binary response variable and...
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Efficient Estimation of the Additive Risks Model for Interval-Censored Data
In contrast to the popular Cox model which presents a multiplicative covariate effect specification on the time to event hazards, the semiparametric...
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A smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure
In this manuscript, we consider a finite multivariate nonparametric mixture model where the dependence between the marginal densities is modeled...
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High-dimensional penalized Bernstein support vector classifier
The support vector machine (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. However, the...
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Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data
We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a...
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An adapted loss function for composite quantile regression with censored data
This paper investigates an adapted loss function for the estimation of a linear regression with right censored responses. The adapted loss function...
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Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions
Mixtures of multivariate leptokurtic-normal distributions have been recently introduced in the clustering literature based on mixtures of elliptical...
