Results 21 to 30 of about 3,128,130 (383)

Sparse Poisson regression via mixed-integer optimization.

PLoS ONE, 2021
We present a mixed-integer optimization (MIO) approach to sparse Poisson regression. The MIO approach to sparse linear regression was first proposed in the 1970s, but has recently received renewed attention due to advances in optimization algorithms and ...
Hiroki Saishu, Kota Kudo, Yuichi Takano
doaj   +1 more source

A New Poisson Generalized Lindley Regression Model

Austrian Journal of Statistics, 2023
In this paper, a new count distribution is introduced. It is a mixture of the Poisson and generalized Lindley distributions. Statistical properties of the proposed distribution including the factorial moments, probability generating function, moment ...
Yupapin Atikankul
doaj   +1 more source

Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models

PLoS Neglected Tropical Diseases, 2021
Background Tsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health ...
Albert Mugenyi, Dennis Muhanguzi, Guy Hendrickx, Gaëlle Nicolas, Charles Waiswa, Steve Torr, Susan Christina Welburn, Peter M. Atkinson   +7 more
doaj   +3 more sources

Bayesian Hurdle Poisson Regression for Assumption Violation

Cauchy: Jurnal Matematika Murni dan Aplikasi, 2022
Violation of the Poisson regression assumption can cause the model formed will produce an unbiased estimator. There is a good method for estimating parameters on small sample sizes and on all distributions, namely the Bayesian method. The number of death
Nur Kamilah Sa'diyah, Ani Budi Astuti, Maria Bernadetha T. Mitakda   +2 more
doaj   +1 more source

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation

F1000Research, 2021
Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Benedicta B Aladeitan, O. Adebimpe, A. Lukman, O. Oludoun, O. Abiodun   +4 more
semanticscholar   +1 more source

OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION

Barekeng, 2023
Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value.
Yesan Tiara, Muhammad Nur Aidi, Erfiani Erfiani, Rika Rachmawati   +3 more
doaj   +1 more source

Bayesian regression of piecewise homogeneous Poisson processes [PDF]

Papers in Physics, 2015
In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data.
Diego Sevilla
doaj   +1 more source

New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications

Axioms, 2022
The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate ...
Issam Dawoud, Fuad A. Awwad, Elsayed Tag Eldin, Mohamed R. Abonazel   +3 more
doaj   +1 more source

PENERAPAN REGRESI GENERALIZED POISSON UNTUK MENGATASI FENOMENA OVERDISPERSI PADA KASUS REGRESI POISSON

E-Jurnal Matematika, 2013
The Poisson regression is generally used to analyze the response variable that is a discrete data. Poisson regression has assumption which must be met, that is condition equidispersion.
I PUTU YUDANTA EKA PUTRA, I PUTU EKA NILA KENCANA, I GUSTI AYU MADE SRINADI   +2 more
doaj   +1 more source

Robust designs for Poisson regression models [PDF]

, 2011
We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates.
Ford I., James M. McGree, John A. Eccleston, Rodriguez-Torreblanca C., Russell K. G., Wang Y.   +5 more
core   +4 more sources