Count data
Repeat tourism in Uruguay: modelling truncated distributions of count data
... through count data regression techniques (see Brida et ...survey. Count data is in fact reported only for those people who visited Uruguay up to five times, whereas bigger values are collapsed ...
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Visualizing count data regressions using rootograms
... in count data ...the data, ...well-known data set from ethology is included, for which a negative binomial hurdle model is ...using data from public health, employs a two-component ...
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Quantile regression for overdispersed count data: a hierarchical method
... persed count data in section 2, before considering quantile regression, using classical methods and in terms of Bayesian implementation (section ...quantile count regression. The remaining sections ...
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Applications of some discrete regression models for count data
... the count data that encounter in the field of Biometrical, Environmental, Social Sciences and Transportation ...crash data which collected on arterial roads in south region (rural) of Florida ...
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Bayesian Count Data Modeling for Finding Technological Sustainability
... study on new patent analysis method based on count data model and Bayesian regression analysis.. 13.[r] ...
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DGEclust: differential expression analysis of clustered count data
... We implemented the methodology presented in this arti- cle in the software package DGEclust, which is written in Python and uses the SciPy stack. DGEclust expects as input and clusters a matrix of unnormalised ...
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Count data regression modeling: an application to spontaneous abortion
... as count data is concerned, studies have been undertaken to find excess variability in the distribution of the outcome count vari- able than what is expected with a Poisson model, and have therefore ...
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Zero-Inflated Models for RNA-Seq Count Data
... A simple and commonly used RNA-seq study includes two treatment conditions in a com- pletely randomized design, for example, treated versus untreated cells. Identification of DE genes unveils the complex functions of ...
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Bayesian Downscaling Methods for Aggregated Count Data
... disaggregated data from aggregated ...land-use data from GIS to facilitate micro-level environmental impact studies, which would otherwise be hindered by data protection rules ...count ...
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A normalization strategy for comparing tag count data
... using data after scaling for the reads per mil- lion (RPM) mapped reads in each ...RPM-normalized data, i.e., the original raw count data should be used as the ...RPM-normalized data ...
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Model selection for time series of count data
... real-life data sets, monthly US polio cases (1970-1983) and monthly benefit claims from the logging industry to the British Columbia Workers Compensation Board (1985-1994) are successfully ...
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Modelling of spatial effects in count data
... in count data models which are typi- cally estimated using Bayesian Markov chain Monte Carlo (MCMC) and applied to a wide range of data, ...crash data (Aguero-Valverde and Jovanis, 2006; ...
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Statistical modelling for falls count data
... epidemiology data describing the magnitude of, and trends in, the problem has largely been descriptive in nature (Boufous et ...injury count data, including falls outcomes, in recent years (Chin and ...
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Modeling zero-inflated and overdispersed count data: application to in-hospital mortality data
... The data provided thus far for studying the effects of serum chloride on hospital and ICU length of stay have not accounted for factors such as regional or geographic ...regarding count data. The ...
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Simulated Minimum Hellinger Distance Inference Methods for Count Data
... for count data. We shall consider grouped count data and ungrouped count ...grouped data, it leads to simulated chi-square type statistics which can be used for model testing for ...
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Scale and shape issues in focused cluster power for count data
... Background: Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Evaluations of statistical power provide important information for the selection ...
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Models for Count Data in the Presence of Outliers and/or Excess Zero
... same data set the indices dropped closer to 1 which indicates that the model fits the data more adequately than the other models in terms of accommodating the problem of ...the data wells in terms of ...
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Extended Poisson–Tweedie:Properties and regression models for count data
... practical data anal- ysis, however, recently some authors have given attention towards the underdisper- sion ...experimental data based on the Gamma-Count ...
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Hermite regression analysis of multi-modal count data
... Two particularly appealing features of the Hermite distribution are that it is capable of modeling multi-modal count data without any modification, and simultaneously it can account for over- dispersion in ...
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Regression Models for Count Data in R
... In R (R Development Core Team 2008), GLMs are provided by the model fitting functions glm() (Chambers and Hastie 1992) in the stats package and glm.nb() in the MASS package (Venables and Ripley 2002) along with ...
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