They used Poisson regression models and standardized incidence ratios for statistical analysis.
What does PRM stand for?
PRM stands for Poisson Regression Model (computational mathematics)
This definition appears frequently and is found in the following Acronym Finder categories:
- Information technology (IT) and computers
- Science, medicine, engineering, etc.
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We have 235 other meanings of PRM in our Acronym Attic
- Personal Radiation Monitor
- Personal Relationship Manager (various oranizations)
- Personnel Readiness Manager
- Petition/Proposal for Rulemaking
- Physical Rule Model
- Physician Relationship Management
- Pipe Rack Module (transportation)
- Pitt Rivers Museum (UK)
- Planning and Resource Management (various organizations)
- Planning Reserve Margin (various organizations)
Samples in periodicals archive:
Poisson regression models were used to calculate incidence rate ratios (IRRs) for disomy by exposure quartiles, controlling for demographic characteristics and semen parameters.
We explored 2 different predictive modeling approaches using past survey counts as the response data: 1) we fit a Poisson regression model with linear and additive effects of land-cover variables (on the log scale); 2) we formed predictions using boosted regression trees (hereafter 'boosted trees'), a non-parametric method that tends to perform well in settings where interactions are prevalent or the relationship between response and predictor variables is highly non-linear (De'ath 2007, Elith et al.
The covariates in the Poisson regression model were examined for 2-way interactions, but none could be confirmed.
Further, as Table 3 only contains the mean and variance of goals, it checks the validity of a simple univariate Poisson model, not accounting for team strengths or the dependence between the individual teams' scoring intensities that is possible with the bivariate Poisson regression model.
Uusipaikka begins as with likelihood-based statistical inference, including likelihood ratio tests and maximum likelihood estimates, then moves to generalized regression models (giving definitions and special cases), the general linear model, including confidence the region's and intervals, nonlinear regression models, generalized linear models, binomial and logistic regression models, Poisson regression models, multinomial regression models, and other generalized regression models, including those which are linear.
To examine the role of subject demographics, a count-data Poisson regression model is estimated using the Ind 10x and phase 1-sequenced IGI data, where the count of safe-lottery choices is the dependent variable.