Search
Search Results
-
Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions
Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster...
-
Model-based clustering for random hypergraphs
A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world...
-
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...
-
Penalized generalized estimating equations approach to longitudinal data with multinomial responses
In high-dimensional longitudinal data with multinomial response, the number of covariates is always much larger than the number of subjects and when...
-
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...
-
Robust MAVE for single-index varying-coefficient models
In this paper, a robust, efficient and easily implemented estimation procedure for single-index varying-coefficient models is proposed by combining...
-
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...
-
Dirichlet compound negative multinomial mixture models and applications
In this paper, we consider an alternative parametrization of Dirichlet Compound Negative Multinomial (DCNM) using rising polynomials. The new...
-
Chimeral Clustering
Hybrid species tend to exhibit a mixture of parent characteristics; we propose chimeral clusters as exhibiting a mixture of parent parameters, a type...
-
Multinomial Restricted Unfolding
For supervised classification we propose to use restricted multidimensional unfolding in a multinomial logistic framework. Where previous research...
-
Parsimonious Finite Mixtures of Matrix-Variate Regressions
Over the years, there has been an increased interest in the analysis of matrix-variate data. In the model-based clustering literature, finite...
-
Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components
We propose a semi-parametric clustering model assuming conditional independence given the component. One advantage is that this model can handle...
-
Is EM really necessary here? Examples where it seems simpler not to use EM
If one is to judge by counts of citations of the fundamental paper (Dempster in JRSSB 39: 1–38, 1977), EM algorithms are a runaway success. But it is...
-
Model-based clustering via new parsimonious mixtures of heavy-tailed distributions
Two families of parsimonious mixture models are introduced for model-based clustering. They are based on two multivariate distributions-the shifted...
-
Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights
Finite mixtures of nonlinear mixed-effects models have emerged as a prominent tool for modeling and clustering longitudinal data following nonlinear...
-
Estimating the class prior for positive and unlabelled data via logistic regression
In the paper, we revisit the problem of class prior probability estimation with positive and unlabelled data gathered in a single-sample scenario....
-
Some new statistical methods for a class of zero-truncated discrete distributions with applications
Counting data without zero category often occurs in various fields. A class of zero-truncated discrete distributions such as the zero-truncated...
-
Modelling rankings in R: the PlackettLuce package
This paper presents the R package PlackettLuce , which implements a generalization of the Plackett–Luce model for rankings data. The generalization...
-
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as...
