Results 21 to 30 of about 293,429 (189)
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 +3 more
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Chapter 20: What do interviewers learn? Changes in interview length and interviewer behaviors over the field period. Appendix 20 [PDF]
, 2020 Appendix 20A Full Model Coefficients and Standard Errors Predicting Count of Questions with Individual Interviewer Behaviors, Two-level Multilevel Poisson Models with Number of Questions Asked as Exposure Variable, WLT1 and WLT2 Analytic strategyTable ...
Olson, Kristen M., Smyth, Jolene
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Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler? [PDF]
, 2005 In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial e ects. The approach is based on Frühwirth-Schnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of ...
Czado, Claudia, Gschlößl, Susanne
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Poisson regression charts for the monitoring of surveillance time series [PDF]
, 2006 This paper presents a Poisson control chart for monitoring time series of counts typically arising in the surveillance of infectious diseases. The in-control mean is assumed to be time-varying and linear on the log-scale with intercept and seasonal ...
Höhle, Michael
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PEMODELAN JUMLAH KEMATIAN BAYI DI PROVINSI MALUKU TAHUN 2010 DENGAN MENGGUNAKAN REGRESI POISSON
Barekeng, 2012 Infant mortality is an experienced child death before the age of one year. Regression analysis is a statistical analysis that aims to model the relationship between response variables (Y) with predictor variables (X).
Salmon N. Aulele
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Introducing and evaluating a Gibbs sampler for spatial Poisson regression models [PDF]
, 2005 In this paper we present a Gibbs sampler for a Poisson model including spatial effects. Frühwirth-Schnatter und Wagner (2004b) show that by data augmentation via the introduction of two sequences of latent variables a Poisson regression model can be ...
Czado, Claudia, Gschlößl, Susanne
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PLoS ONE, 2022 In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency.
Daisuke Murakami, Tomoko Matsui
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EM Estimation for Zero- and k-Inflated Poisson Regression Model
Computation, 2021 Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the
Monika Arora, N. Rao Chaganty
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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. +5 more
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Modified Ridge Estimator for Poisson Regression
Cumhuriyet Science JournalPoisson regression is a statistical model used to model the relationship between a count-valued-dependent variable and one or more independent variables. A frequently encountered problem when modeling such relationships is multicollinearity, which occurs
Shuaib Mursal Ibrahim, Aydın Karakoca
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