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A best linear threshold classification with scale mixture of skew normal populations
This paper describes a threshold classification with $$K$$ K populations whose membership category is associated with the threshold process of a latent variable. It is seen that the optimal procedure (Bayes procedure) for the classification involves a ...
A GS-CORE algorithm for performing a reduction test on multiple gene sets and their core genes
Gene-set analysis seeks to identify enriched gene sets that are strongly associated with the phenotype. In many applications, only a small subset of core genes in each enriched gene set is likely associated with the phenotype. The reduction of enriched ...
Variable selection for varying-coefficient models with the sparse regularization
Varying-coefficient models are useful tools for analyzing longitudinal data. They can effectively describe a relationship between predictors and responses which are repeatedly measured. We consider the problem of selecting variables in the varying-...
S-estimation of hidden Markov models
A method for robust estimation of dynamic mixtures of multivariate distributions is proposed. The EM algorithm is modified by replacing the classical M-step with high breakdown S-estimation of location and scatter, performed by using the bisquare ...
Study of compound generalized Nakagami---generalized inverse Gaussian distribution and related densities: application to ultrasound imaging
A new theoretical probability distribution generalized Nakagami---generalized inverse Gaussian distribution (GN---GIGD) is proposed to model the backscattered echo envelope in ultrasound imaging. This new probability distribution is a composite ...
Influence measure based on probabilistic behavior of regression estimators
An influence measure for investigating the influence of deleting an observation in linear regression is proposed based on geometric thoughts of the sampling distribution of the distance between two estimators of regression coefficients computed with and ...
Estimating cell probabilities in contingency tables with constraints on marginals/conditionals by geometric programming with applications
Contingency tables are often used to display the multivariate frequency distribution of variables of interest. Under the common multinomial assumption, the first step of contingency table analysis is to estimate cell probabilities. It is well known that ...
On the genetic algorithm with adaptive mutation rate and selected statistical applications
We give sufficient conditions which the mutation rate must satisfy for the convergence of the genetic algorithm when that rate is allowed to change throughout iterations. The empirical performance of the algorithm with regards to changes in the mutation ...
An EM algorithm for the estimation of parameters of a flexible cure rate model with generalized gamma lifetime and model discrimination using likelihood- and information-based methods
In this paper, we consider the Conway---Maxwell Poisson (COM-Poisson) cure rate model based on a competing risks scenario. This model includes, as special cases, some of the well-known cure rate models discussed in the literature. By assuming the time-...
Variable selection after screening: with or without data splitting?
High dimensional data sets are now frequently encountered in many scientific fields. In order to select a sparse set of predictors that have predictive power and/or provide insightful understanding on which predictors really influence the response, a ...
Bayesian structured variable selection in linear regression models
In this paper we consider the Bayesian approach to the problem of variable selection in normal linear regression models with related predictors. We adopt a generalized singular $$g$$g-prior distribution for the unknown model parameters and the beta-...
Model evaluation, discrepancy function estimation, and social choice theory
A discrepancy function provides for an evaluation of a candidate model by quantifying the disparity between the candidate model and the true model that generated the observed data. The favored model from a candidate class is the one judged to have ...
Association rule mining through adaptive parameter control in particle swarm optimization
Association rule mining is a data mining task on a great deal of academic research has been done and many algorithms are proposed. Association rule mining is treated as a twofold process by most of the methods. It increases the complexity of the system ...
Generalized data-fitting factor analysis with multiple quantification of categorical variables
In this study, a recently proposed data-fitting factor analysis (DFFA) procedure is generalized for categorical variable analysis. For generalized DFFA (GDFFA), we develop an alternating least squares algorithm consisting of a multiple quantification ...