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Geometric infinitely divisible autoregressive models
In this article, we discuss some geometric infinitely divisible (gid) random variables using the Laplace exponents which are Bernstein functions and...
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Bayesian finite mixtures of Ising models
We introduce finite mixtures of Ising models as a novel approach to study multivariate patterns of associations of binary variables. Our proposed...
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Bridging techniques in the redesign of the Italian Survey on Household Income and Wealth
The design of the Bank of Italy’s Survey on Household Income and Wealth was revised in 2020 to reduce non-sampling errors in households’ income and...
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A Retailer’s Deteriorating Inventory Model with Amelioration and Permissible Backlogging Under Power Pattern Demand
There are many ways to handle an inventory problem, starting with mathematical and simulation strategies that include trial and error techniques....
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Linear hypothesis testing in ultra high dimensional generalized linear mixed models
This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the...
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Parametric estimation for linear parabolic SPDEs in two space dimensions based on temporal and spatial increments
We deal with parameter estimation for linear parabolic second-order stochastic partial differential equations in two space dimensions driven by two...
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Statistical inference for linear quantile regression with measurement error in covariates and nonignorable missing responses
In this paper, we consider quantile regression estimation for linear models with covariate measurement errors and nonignorable missing responses....
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The Extended Bregman Divergence and Parametric Estimation in Continuous Models
Under standard regularity conditions, the maximum likelihood estimator (MLE) is the most efficient estimator at the model. However, modern practice...
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SNN-PDM: An Improved Probability Density Machine Algorithm Based on Shared Nearest Neighbors Clustering Technique
Probability density machine (PDM) is a novel algorithm which was proposed recently for addressing class imbalance learning (CIL) problem. PDM can...
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FPDclustering: a comprehensive R package for probabilistic distance clustering based methods
Data clustering has a long history and refers to a vast range of models and methods that exploit the ever-more-performing numerical optimization...
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Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior
When asked to assess their opinion about attitudes or perceptions on Likert-scale, respondents often endorse the midpoint or extremes of the scale...
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Analyzing quantitative performance: Bayesian estimation of 3-component mixture geometric distributions based on Kumaraswamy prior
This research addresses the underutilization of discrete life testing models and proposes a Bayesian estimation strategy for a 3-component mixture of...
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Using Density and Fuzzy Clustering for Data Cleaning and Segmental Description of Livestock Data
The cluster algorithms density-based clustering with noise and fuzzy c-means were used to edit and group a large, noisy data set from a livestock...
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Efficient regression analyses with zero-augmented models based on ranking
Several zero-augmented models exist for estimation involving outcomes with large numbers of zero. Two of such models for handling count endpoints are...
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A Collocation Method for Nonlinear Stochastic Differential Equations Driven by Fractional Brownian Motion and its Application to Mathematical Finance
The main aim of this article is to demonstrate the collocation method based on the barycentric rational interpolation function to solve nonlinear...
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Deriving the Distribution and Exploring the Utility of Partial \(R^2\) in the Era of Big Data
A central goal in the world of statistics and data science is the construction of linear regression models for continuous variables of interest....
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Generalized mixed spatiotemporal modeling with a continuous response and random effect via factor analysis
This work focuses on Generalized Linear Mixed Models that incorporate spatiotemporal random effects structured via Factor Model (FM) with nonlinear...
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Prediction in non-sampled areas under spatial small area models
In this article we study the prediction problem in small geographic areas in the situation where the survey data does not cover a substantial...
