The Poisson distribution is commonly used to model the number of expected events for a process given we know the average rate at which events occur during a given unit of time. The Poisson model is often used for Poisson regression, logistic regression, and the Poisson probability mass function...
Poisson Regression involves regression models in which the response variable is in the form of counts and not fractional numbers. For example, the count of number of births or number of wins in a football match series. Also the values of the response variables follow a Poisson distribution....
The maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). In this study, we propose a new estimator with some biasing parameters to estimate the regression coefficients for the PRM when there is multicollinea...
The Poisson Regression Model (PRM) is one of the benchmark models when analyzing the count data. The Maximum Likelihood Estimator (MLE) is used to estimate the model parameters in PRMs. However, the MLE may suffer from various drawbacks that arise due to the existence of multicollinearity probl...
On the estimation of mixtures of Poisson regression models with large number of components. Comput Stat Data Anal. 2014;93:97–106. 13. Si Y, Liu P, Li P, Brutnell TP. Model-based clustering for RNA-seq data. Bioinformatics. 2014;30:197–205. 14. Rau A, Maugis-Rabusseau C, Martin...
[22] used a combination of GAN and regression models to discover cellular metamaterials with superior strength and recovery stress. Their training dataset included 1,500 lattice units formed by representative volume elements and their corresponding mechanical properties. Tian et al. [18] utilized a ...
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glm regression Object detection and tracking in Python Over six months ago I decided to embark on a learning journey of image analysis using Python. After carefully reviewing various options I took a two-course offer from OpenCV.org for about US$479, chiefly because of i) the pivotal role ...
This paper shows how one\ncan fit models to particle data by means of Poisson regression while\ntaking into account chip level correlations and wafer-to-wafer\nvariability. It is shown that when these factors are not taken into\naccount, the model specified does not fit the data properly ...
This study introduces a new two-parameter Liu estimator (PMTPLE) for addressing the multicollinearity problem in the Poisson regression model (PRM). The estimation of the PRM is traditionally accomplished through the Poisson maximum likelihood estimator (PMLE). However, when the explanatory variables ...