A new electivity index for diet studies that use count data
Sign Up to like & getrecommendations! Published in 2021 at "Limnology and Oceanography: Methods"
DOI: 10.1002/lom3.10446
Abstract: Electivity indices summarize the results of field‐based feeding studies by comparing the relative abundance of a potential prey item with its relative prevalence in the diet of a predator. We developed a new electivity index… read more here.
Keywords: new electivity; electivity; index; electivity index ... See more keywords
Comments on "Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros".
Sign Up to like & getrecommendations! Published in 2018 at "Statistics in medicine"
DOI: 10.1002/sim.7321
Abstract: Kassahun et al. [1] proposed a two-level marginalized hurdle combined model for analysis of zeroinflated overdispersed correlated count data. Specifically, the zero-inflation in the count data is accounted for by utilizing a two-part hurdle Poisson… read more here.
Keywords: level; model; overdispersed correlated; count data ... See more keywords
Modeling event count data in the presence of informative dropout with application to bleeding and transfusion events in myelodysplastic syndrome.
Sign Up to like & getrecommendations! Published in 2017 at "Statistics in medicine"
DOI: 10.1002/sim.7351
Abstract: In many biomedical studies, it is often of interest to model event count data over the study period. For some patients, we may not follow up them for the entire study period owing to informative… read more here.
Keywords: count data; informative dropout; event count; dropout ... See more keywords
The mixed model for the analysis of a repeated‐measurement multivariate count data
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in Medicine"
DOI: 10.1002/sim.8101
Abstract: Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less… read more here.
Keywords: model; multivariate count; count data; mixed model ... See more keywords
Blinded sample size re‐estimation for comparing over‐dispersed count data incorporating follow‐up lengths
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9584
Abstract: Blinded sample size re‐estimation (BSSR) is an adaptive design to prevent the power reduction caused by misspecifications of the nuisance parameters in the sample size calculation of comparative clinical trials. However, conventional BSSR methods used… read more here.
Keywords: sample size; blinded sample; count data; size ... See more keywords
Model selection and application to high-dimensional count data clustering
Sign Up to like & getrecommendations! Published in 2018 at "Applied Intelligence"
DOI: 10.1007/s10489-018-1333-9
Abstract: EDCM, the Exponential-family approximation to the Dirichlet Compound Multinomial (DCM), proposed by Elkan (2006), is an efficient statistical model for high-dimensional and sparse count data. EDCM models take into account the burstiness phenomenon correctly while… read more here.
Keywords: high dimensional; model; model selection; count data ... See more keywords
Estimating Willingness to Pay from Count Data When Survey Responses are Rounded
Sign Up to like & getrecommendations! Published in 2020 at "Environmental and Resource Economics"
DOI: 10.1007/s10640-020-00403-6
Abstract: Recall data of visits to recreational sites often contain reported numbers that appear to be rounded to nearby focal points (e.g., the closest 5 or 10). Failure to address this rounding has been shown to… read more here.
Keywords: count data; poisson model; willingness pay;
Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data
Sign Up to like & getrecommendations! Published in 2018 at "Analytic Methods in Accident Research"
DOI: 10.1016/j.amar.2018.04.002
Abstract: Abstract The existence of preponderant zero crash sites and/or sites with large crash counts can present challenges during the statistical analysis of crash count data. Additionally, unobserved heterogeneity in crash data due to the absence… read more here.
Keywords: crash; model; negative binomial; count data ... See more keywords
Can Generalized Poisson model replace any other count data models? An evaluation
Sign Up to like & getrecommendations! Published in 2021 at "Clinical Epidemiology and Global Health"
DOI: 10.1016/j.cegh.2021.100774
Abstract: Abstract Background Count data represents the number of occurrences of an event within a fixed period of time. In count data modelling, overdispersion is inevitable. Sometimes, this overdispersion may not be just due to the… read more here.
Keywords: time; generalized poisson; poisson model; model ... See more keywords
Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches
Sign Up to like & getrecommendations! Published in 2018 at "Ecological Modelling"
DOI: 10.1016/j.ecolmodel.2018.02.007
Abstract: Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic… read more here.
Keywords: bias; heterogeneity; fitting mixture; count data ... See more keywords
Flexible Bayesian Dirichlet mixtures of generalized linear mixed models for count data
Sign Up to like & getrecommendations! Published in 2021 at "Scientific African"
DOI: 10.1016/j.sciaf.2021.e00963
Abstract: Abstract The need to model count data correctly calls for the introduction of a flexible yet a strong model that can sufficiently handle various types of count data. Models such as Ordinary Least Squares (OLS)… read more here.
Keywords: generalized linear; bayesian dirichlet; mixed models; count data ... See more keywords