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Regression analysis of clustered panel count data with additive mean models
In biomedical studies, panel count data have been extensively investigated. Such data occur if study subjects are monitored or observed only at some...
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Bootstrap** generalized linear models to accommodate overdispersed count data
When modelling counts or rates using Poisson regression, it is common to find overdispersion in data. Overdispersed count data is prevalent in a...
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Supervised Classification of High-Dimensional Correlated Data: Application to Genomic Data
This work addresses the problem of supervised classification for high-dimensional and highly correlated data using correlation blocks and supervised...
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Modeling Negatively Skewed Survival Data in Accelerated Failure Time and Correlated Frailty Models
Negatively skewed survival data arise in public health and in statistical research. Commonly used distributions are not always well suited to this...
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A flexible multivariate model for high-dimensional correlated count data
We propose a flexible multivariate stochastic model for over-dispersed count data. Our methodology is built upon mixed Poisson random vectors ( Y 1 ,…, Y d ...
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Polynomial spline estimation of panel count data model with an unknown link function
Panel count data are frequently encountered in follow-up studies such as clinical trials, reliability researches, and insurance studies. Models about...
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Copula-based bivariate finite mixture regression models with an application for insurance claim count data
Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but...
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Bayesian local bandwidths in a flexible semiparametric kernel estimation for multivariate count data with diagnostics
In this paper, we consider a flexible semiparametric approach for estimating multivariate probability mass functions. The corresponding estimator is...
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Zero-inflated Poisson-Akash distribution for count data with excessive zeros
Over-dispersed models are often used whenever the variation is more than what in point of fact is anticipated by a model. One of the reasons behind...
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Doubly-Inflated Poisson INGARCH Models for Count Time Series
Researchers usually model count time series data assuming the responses follow the Poisson distribution. The Poisson distribution is often a suitable...
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Residual Diagnostic Methods for Bell-Type Count Models
Count datasets represented as integers are commonly encountered in various scientific fields, encompassing scenarios such as the number of species in...
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Semiparametric analysis of multivariate panel count data with nonlinear interactions
Multivariate panel count data frequently arise in follow up studies involving several related types of recurrent events. For univariate panel count...
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Standard GEE Modeling of Correlated Univariate Outcomes
Formulations are provided for correlated sets of univariate outcomes allowing for missing values, generalized linear modeling of means for such...
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Introducing LASSO-type penalisation to generalised joint regression modelling for count data
In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the
R packageGJRM by Marra and...
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Specifications tests for count time series models with covariates
We propose a goodness-of-fit test for a class of count time series models with covariates which includes the Poisson autoregressive model with...
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Multivariate count time series segmentation with “sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub
This paper deals with a clustering approach based on mixture models to analyze multidimensional mobility count time-series data within a multimodal...
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Improved inference for areal unit count data using graph-based optimisation
Spatio-temporal count data relating to a set of non-overlap** areal units are prevalent in many fields, including epidemiology and social science....
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Multivariate Count Data Regression Models and Their Applications
Multivariate regression models based on multivariate discrete distributions will be defined and studied. Multivariate discrete distributions...
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Non-Independent Data
Many infectious disease experiments result in non-independent data because of spatial autocorrelationAutocorrelation across fields (such as...
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A class of models for large zero-inflated spatial data
Spatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include...
