Results 311 to 320 of about 3,145,580 (383)
Some of the next articles are maybe not open access.
Developing robust ridge estimators for Poisson regression model
Concurrency and Computation, 2022The Poisson regression model (PRM) is the standard statistical method of analyzing count data, and it is estimated by a Poisson maximum likelihood (PML) estimator.
M. R. Abonazel, I. Dawoud
semanticscholar +1 more source
Concurrency and Computation, 2021
In data analysis, count data modeling contributing a significant role. The Conway‐Maxwell Poisson (COMP) is one of the flexible count data models to deal over and under dispersion.
F. Sami +2 more
semanticscholar +1 more source
In data analysis, count data modeling contributing a significant role. The Conway‐Maxwell Poisson (COMP) is one of the flexible count data models to deal over and under dispersion.
F. Sami +2 more
semanticscholar +1 more source
On the James-Stein estimator for the poisson regression model
Communications in statistics. Simulation and computation, 2020The Poisson regression model (PRM) aims to model a counting variable y, which is usually estimated by using maximum likelihood estimation (MLE) method. The performance of MLE is not satisfactory in the presence of multicollinearity. Therefore, we propose
M. Amin, M. Akram, M. Amanullah
semanticscholar +1 more source
Zero-inflacted Poisson regression, with an application to defects in manufacturing
, 1992Zero-inflated Poisson (ZIP) regression is a model for count data with excess zeros. It assumes that with probability p the only possible observation is 0, and with probability 1 – p, a Poisson(λ) random variable is observed.
D. Lambert
semanticscholar +1 more source
A Comparative Study of Robust Estimators for Poisson Regression Model with Outliers
Journal of Statistics Applications & Probability, 2020The present paper considers Poisson regression model in case of the dataset that contains outliers. The Monte Carlo simulation study was conducted to compare the robust (Mallows quasi-likelihood, weighted maximum likelihood) estimators with the nonrobust
M. R. Abonazel, O. Saber
semanticscholar +1 more source
Conway–Maxwell–Poisson regression models for dispersed count data
WIREs Computational Statistics, 2020While Poisson regression serves as a standard tool for modeling the association between a count response variable and explanatory variables, it is well‐documented that this approach is limited by the Poisson model's assumption of data equi‐dispersion ...
Kimberly F. Sellers, Bailey Premeaux
semanticscholar +1 more source
Understanding Poisson Regression
Journal of Nursing Education, 2014Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur.
Matthew J. Hayat, Melinda Higgins
openaire +2 more sources
ADDRESSING IMBALANCED INSURANCE DATA THROUGH ZERO-INFLATED POISSON REGRESSION WITH BOOSTING
ASTIN Bulletin: The Journal of the International Actuarial Association, 2020A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need
Simon C. K. Lee
semanticscholar +1 more source
Hierarchical Poisson Regression Modeling [PDF]
Journal of the American Statistical Association, 1997 Abstract The Poisson model and analyses here feature nonexchangeable gamma distributions (although exchangeable following a scale transformation) for individual parameters, with standard deviations proportional to means. A relatively uninformative prior distribution for the shrinkage values eliminates the ill behavior of maximum likelihood estimators ...
Carl N. Morris, Cindy L. Christiansen
openaire +1 more source