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Relevant bibliographies by topics / Pseudo-Poisson Maximum Likelihood

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Academic literature on the topic 'Pseudo-Poisson Maximum Likelihood'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Pseudo-Poisson Maximum Likelihood"

1

Pfaffermayr, Michael. "Constrained Poisson pseudo maximum likelihood estimation of structural gravity models." International Economics 161 (May 2020): 188–98. http://dx.doi.org/10.1016/j.inteco.2019.11.014.

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2

Prehn, S., B. Brümmer, and T. Glauben. "Gravity model estimation: fixed effects vs. random intercept Poisson pseudo-maximum likelihood." Applied Economics Letters 23, no. 11 (2015): 761–64. http://dx.doi.org/10.1080/13504851.2015.1105916.

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3

Santos Silva, J. M. C., and Silvana Tenreyro. "Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator." Economics Letters 112, no. 2 (2011): 220–22. http://dx.doi.org/10.1016/j.econlet.2011.05.008.

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4

Timsina, Krishna P., and Richard J. Culas. "Do Free Trade Agreements Increase Australian Trade: An Application of Poisson Pseudo Maximum Likelihood Estimator?" Journal of East-West Business 26, no. 1 (2019): 56–80. http://dx.doi.org/10.1080/10669868.2019.1685056.

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5

Fauziah, Ghina, and Siti Sunendiari. "Estimasi Pseudo Poisson Maximum Likelihood untuk Mengatasi Masalah dalam Model Log-Linear pada Kasus Kusta di Jawa Barat Tahun 2018." Jurnal Riset Statistika 1, no. 1 (2021): 57–62. http://dx.doi.org/10.29313/jrs.v1i1.147.

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Abstract. Poisson regression is a statistical method used to analyze the relationship between the response variable and the predictor variable where the data from the response variable is in the form of count data and follows the Poisson distribution. Poisson regression is used to model rare or rare events, so that the response variable is very likely to have a value of zero. Poisson regression is a regression model whose response variable is non-negative. Usually, this model fits the linear regression applied to the log-transformed response variable. However, when the response variable data h

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6

Motta, Victor. "Estimating Poisson pseudo-maximum-likelihood rather than log-linear model of a log-transformed dependent variable." RAUSP Management Journal 54, no. 4 (2019): 508–18. http://dx.doi.org/10.1108/rausp-05-2019-0110.

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Purpose The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as bein

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Kumar, Pushp, Naresh Chandra Sahu, and Mohd Arshad Ansari. "Export Potential of Climate Smart Goods in India: Evidence from the Poisson Pseudo Maximum Likelihood Estimator." International Trade Journal 35, no. 3 (2021): 288–308. http://dx.doi.org/10.1080/08853908.2021.1890652.

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8

Bugoma, Suwadu, Noureddine Abdellatif, and Gilbert Niyongabo. "Determinants of imports in East African community: a comparative analysis using Poisson pseudo maximum likelihood estimator." Applied Mathematical Sciences 16, no. 12 (2022): 679–700. http://dx.doi.org/10.12988/ams.2022.917268.

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9

Dlamini, Sabelo Nick, Wisdom Mdumiseni Dlamini, and Ibrahima Socé Fall. "Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method." International Journal of Environmental Research and Public Health 19, no. 15 (2022): 9171. http://dx.doi.org/10.3390/ijerph19159171.

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COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the

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10

Correia, Sergio, Paulo Guimarães, and Tom Zylkin. "Fast Poisson estimation with high-dimensional fixed effects." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (2020): 95–115. http://dx.doi.org/10.1177/1536867x20909691.

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In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package ( Correia, 2014 , Statistical Software Components S457874, Department of Economics, Boston College), it has similar syntax, supports many of the same functionalities, and benefits from reghdfe‘s fast convergence properties for

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