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Journal articles on the topic 'Poisson pseudo-maximum likelihood estimator'

Author: Grafiati

Published: 7 September 2021

Last updated: 29 July 2025

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1

Jumamyradov, Maksat, and Murat K. Munkin. "Biases in Maximum Simulated Likelihood Estimation of Bivariate Models." Journal of Econometric Methods 11, no. 1 (2021): 55–70. http://dx.doi.org/10.1515/jem-2021-0003.

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Abstract This paper finds that the maximum simulated likelihood (MSL) estimator produces substantial biases when applied to the bivariate normal distribution. A specification of the random parameter bivariate normal model is considered, in which a direct comparison between the MSL and maximum likelihood (ML) estimators is feasible. The analysis shows that MSL produces biased results for the correlation parameter. This paper also finds that the MSL estimator is biased for the bivariate Poisson-lognormal model, developed by Munkin and Trivedi (1999. “Simulated Maximum Likelihood Estimation of Mu

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2

Mohammed Alomari, Huda. "Bayes Estimations for Parameter of the Poisson distribution with Progressive Schemes." Academic Journal of Applied Mathematical Sciences, no. 102 (October 9, 2024): 14–23. https://doi.org/10.32861/ajams.10.2.14.23.

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This study introduces maximum likelihood and Bayesian approaches to Poisson parameter estimation using posterior distribution. I discuss three types of loss functions: the asymmetric linear exponential loss function, non-linear exponential loss function, and squared error loss function. Their performance is compared with the maximum likelihood estimator using mean squared error (MSE) as the test criterion. The proposed method with the classical estimator (maximum likelihood estimator) is better than that with the non-classical estimators for point estimation with different sample sizes. Maximu

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Islamov, Bakhtiyor, and Munisa Turdibaeva. "ECONOMETRIC ESTIMATION OF THE GRAVITY MODEL OF EXPORTS: A CASE OF THE REPUBLIC OF UZBEKISTAN." Economics and Education 24, no. 3 (2023): 510–17. http://dx.doi.org/10.55439/eced/vol24_iss3/a81.

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In this article, the authors critically analyze the existing econometric approaches to estimating gravity models of exports from the point of view of solving such problems as taking into account zero observations, bias, inconsistency and inefficiency of estimates. As a result, the authors come to the conclusion that the method of Poisson pseudo-maximum-likelihood estimation (Poisson pseudo-maximum-likelihood, PPML) is the most preferable method for estimating the gravity export model of the Republic of Uzbekistan.

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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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5

Recalde, María Luisa, Marcelo Florensa, and Ivan Iturralde. "Gravity Equation and Trade Agreements: A Different Econometric Approach." Revista de Economía y Estadística 46, no. 2 (2008): 83–103. http://dx.doi.org/10.55444/2451.7321.2008.v46.n2.3853.

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This paper is aimed at multiplicatively estimating the parameters of the gravity equation by using the Poisson Pseudo-Maximum-Likelihood (PPML) estimator, and taking heteroskedasticity into account at the same time. Besides, in order to compare the results, the model will be estimated trough OLS and Tobit, and the precision of the different estimators will be assessed by a set of specification tests. Results indicate that the effects of Preferential Trade Agreements are very sensitive to the method chosen to estimate the gravity model and the results obtained under PPML are the most reliable.

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Youssef, Ahmed Hassen, Mohamed Reda Abonazel, and Elsayed G. Ahmed. "Robust M Estimation for Poisson Panel Data Model with Fixed Effects: Method, Algorithm, Simulation, and Application." Statistics, Optimization & Information Computing 12, no. 5 (2024): 1292–305. http://dx.doi.org/10.19139/soic-2310-5070-1996.

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The fixed effects Poisson (FEP) model is one of the most important for the count data when the data containperiods and cross-sectional units. The maximum likelihood (ML) estimation method for the FEP model provides good results in the absence of outliers, but it is affected by outliers. So, we introduce in this paper robust estimators for the FEP model. These estimators yield stable and good results in case of the presence of outliers. The Monte Carlo simulation study and empirical application were conducted to assess the performance of the non-robust fixed Poisson maximum likelihood (FPML) es

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7

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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8

O. E., Obodo,, and Umeh, E. U. "Assessing the Variance of Maximum Likelihood Estimates for Truncated Pseudo-Lindley Poisson Distribution: A Simulation-Based Approach." African Journal of Mathematics and Statistics Studies 8, no. 2 (2025): 67–84. https://doi.org/10.52589/ajmss-55e9rvcv.

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This paper introduces a new lifetime probability distribution called the Truncated Pseudo-Lindley-Poisson Distribution (TPLPD), which generalises the Pseudo-Lindley Distribution and the Poisson distribution. The distribution is a flexible distribution used to model count data with varying degrees of dispersion. This study also investigates the performance of Maximum Likelihood Estimation (MLE) for estimating the parameters of the Truncated Pseudo Lindley Poisson Distribution (TPLPD) through a simulation-based approach. The variance of MLE estimates is assessed under various sample sizes and pa

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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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10

Santos, José António, and M. Manuela Neves. "A local maximum likelihood estimator for Poisson regression." Metrika 68, no. 3 (2007): 257–70. http://dx.doi.org/10.1007/s00184-007-0156-1.

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11

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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12

Akram, Muhammad Nauman, Muhammad Amin, Faiza Sami, Adam Braima Mastor, Omer Mohamed Egeh, and Abdisalam Hassan Muse. "A New Conway Maxwell–Poisson Liu Regression Estimator—Method and Application." Journal of Mathematics 2022 (March 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/3323955.

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Poisson regression is a popular tool for modeling count data and is applied in medical sciences, engineering and others. Real data, however, are often over or underdispersed, and we cannot apply the Poisson regression. To overcome this issue, we consider a regression model based on the Conway–Maxwell Poisson (COMP) distribution. Generally, the maximum likelihood estimator is used for the estimation of unknown parameters of the COMP regression model. However, in the existence of multicollinearity, the estimates become unstable due to its high variance and standard error. To solve the issue, a n

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Kiderlen, Markus. "Non-parametric estimation of the directional distribution of stationary line and fibre processes." Advances in Applied Probability 33, no. 1 (2001): 6–24. http://dx.doi.org/10.1017/s0001867800010600.

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Two non-parametric methods for the estimation of the directional measure of stationary line and fibre processes in d-dimensional space are presented. The input data for both methods are intersection counts with finitely many test windows situated in hyperplanes. The first estimator is a measure valued maximum likelihood estimator, if applied to Poisson line processes. The second estimator uses an approximation of the associated zonoid (the Steiner compact) by zonotopes. Consistency of both estimators is proved (without use of the Poisson assumption). The estimation methods are compared empiric

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Al-obedy, Jinan A. Naser. "Posterior Estimates for the Parameter of the Poisson Distribution by Using Two Different Loss Functions." Ibn AL- Haitham Journal For Pure and Applied Sciences 35, no. 1 (2022): 60–72. http://dx.doi.org/10.30526/35.1.2800.

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In this paper, Bayes estimators of Poisson distribution have been derived by using two loss functions: the squared error loss function and the proposed exponential loss function in this study, based on different priors classified as the two different informative prior distributions represented by erlang and inverse levy prior distributions and non-informative prior for the shape parameter of Poisson distribution. The maximum likelihood estimator (MLE) of the Poisson distribution has also been derived. A simulation study has been fulfilled to compare the accuracy of the Bayes estimates with the

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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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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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17

Joshi, Ashwini, Angelika Geroldinger, Lena Jiricka, Pralay Senchaudhuri, Christopher Corcoran, and Georg Heinze. "Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression." Statistical Methods in Medical Research 31, no. 2 (2021): 253–66. http://dx.doi.org/10.1177/09622802211065405.

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Poisson regression can be challenging with sparse data, in particular with certain data constellations where maximum likelihood estimates of regression coefficients do not exist. This paper provides a comprehensive evaluation of methods that give finite regression coefficients when maximum likelihood estimates do not exist, including Firth’s general approach to bias reduction, exact conditional Poisson regression, and a Bayesian estimator using weakly informative priors that can be obtained via data augmentation. Furthermore, we include in our evaluation a new proposal for a modification of Fi

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18

Abdullahi, Ibrahim, and Shamsuddeen Ahmad Sabo. "On The Existence and Uniqueness Conditions of The MLEs of The Four-Parameter Exponentiated BurrXII-Poisson Distribution." International Journal of Science for Global Sustainability 10, no. 3 (2024): 57–62. https://doi.org/10.57233/ijsgs.v10i3.697.

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A maximum likelihood estimator is guaranteed to exist, provided specific regularity conditions are met to ensure a well-defined likelihood function and achievable maximization. This paper considered the existence and uniqueness of the maximum likelihood estimates (MLE) of the exponentiated BurrXII Poisson (EBXIIP) distribution under some possible conditions.

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19

Chiu, Chun-Huo. "A more reliable species richness estimator based on the Gamma–Poisson model." PeerJ 11 (January 6, 2023): e14540. http://dx.doi.org/10.7717/peerj.14540.

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Background Accurately estimating the true richness of a target community is still a statistical challenge, particularly in highly diverse communities. Due to sampling limitations or limited resources, undetected species are present in many surveys and observed richness is an underestimate of true richness. In the literature, methods for estimating the undetected richness of a sample are generally divided into two categories: parametric and nonparametric estimators. Imposing no assumptions on species detection rates, nonparametric methods demonstrate robust statistical performance and are widel

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20

Huelsenbeck, John P., and Bruce Rannala. "Maximum likelihood estimation of phylogeny using stratigraphic data." Paleobiology 23, no. 2 (1997): 174–80. http://dx.doi.org/10.1017/s0094837300016778.

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The stratigraphic distribution of fossil species contains potential information about phylogeny because some phylogenetic trees are more consistent with the distribution of fossils in the rock record than others. A maximum likelihood estimator of phylogeny is derived using an explicit mathematical model of fossil preservation. The method assumes that fossil preservations within lineages follow an independent Poisson process, but can be extended to include other preservation models. The performance of the method was examined using Monte Carlo simulation. The performance of the maximum likelihoo

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21

Sami, Faiza, Muhammad Amin, and Sadiah M. A. Aljeddani. "Stein estimation in the Conway-Maxwell Poisson model with correlated regressors." International Journal of ADVANCED AND APPLIED SCIENCES 11, no. 7 (2024): 49–56. http://dx.doi.org/10.21833/ijaas.2024.07.006.

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The Poisson regression model (PRM) is widely used for count data, applicable when the response variable follows a Poisson distribution with equal dispersion. The Conway-Maxwell Poisson regression model (COMPRM) is more flexible and can handle both under-dispersion and over-dispersion. However, the COMPRM may involve correlated regressors, leading to multicollinearity, which makes the maximum likelihood estimator (MLE) inefficient. Biased estimation methods can address multicollinearity in data. This study proposes a Stein estimator, a biased estimation method, for the COMPRM that can simultane

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22

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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23

Balestra, Simone, and Uschi Backes-Gellner. "When a Door Closes, a Window Opens? Long-Term Labor Market Effects of Involuntary Separations." German Economic Review 18, no. 1 (2017): 1–21. http://dx.doi.org/10.1111/geer.12086.

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Abstract This study estimates the earning losses of workers experiencing an involuntary job separation. We employ, for the first time in the earning losses literature, a Poisson pseudo-maximum-likelihood estimator with fixed effects that has several advantages with respect to conventional fixed effects models. The Poisson estimator allows considering the full set of involuntary separations, including those with zero labor market earnings because of unemployment. By including individuals with zero earnings and by using our new method, the loss in the year of separation becomes larger than in pr

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24

Dawoud, Issam, Fuad A. Awwad, Elsayed Tag Eldin, and Mohamed R. Abonazel. "New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications." Axioms 11, no. 11 (2022): 612. http://dx.doi.org/10.3390/axioms11110612.

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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 this problem. Additionally, the PML estimator is sensitive to multicollinearity. Therefore, several biased Poisson estimators have been provided to cope with this problem, such as the Poisson ridge estimator, Poisson Liu estimator, Poisson Kibria–Lukman estimator, and Poisson modified Kibria–Lukman estimator. Despite different Poisson biased reg

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Espinosa, Sebastian, Jorge F. Silva, Rene A. Mendez, Rodrigo Lobos, and Marcos Orchard. "Optimality of the maximum likelihood estimator in astrometry." Astronomy & Astrophysics 616 (August 2018): A95. http://dx.doi.org/10.1051/0004-6361/201732537.

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Context. Astrometry relies on the precise measurement of the positions and motions of celestial objects. Driven by the ever-increasing accuracy of astrometric measurements, it is important to critically assess the maximum precision that could be achieved with these observations. Aims. The problem of astrometry is revisited from the perspective of analyzing the attainability of well-known performance limits (the Cramér–Rao bound) for the estimation of the relative position of light-emitting (usually point-like) sources on a charge-coupled device (CCD)-like detector using commonly adopted estima

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26

Xu, Zheng. "An expectation-maximization algorithm for logistic regression based on individual-level predictors and aggregate-level response." Statistics in Transition new series 26, no. 1 (2025): 9–28. https://doi.org/10.59139/stattrans-2025-002.

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Logistic regression is widely used in complex data analysis. When predictors are at individual level and the response at aggregate level, logistic regression can be estimated using the Maximum Likelihood Estimation (MLE) method with the joint likelihood function formed by Poisson binomial distributions. When directly maximizing the complicated likelihood function, the performance of MLE will worsen as the number of predictors increases. In this article, we propose an expectation-maximization (EM) algorithm to avoid the direct maximization of the complicated likelihood function. Simulation stud

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Sangnawakij, Patarawan. "Parametric and Nonparametric Estimation of Population Mean in Poisson-Xgamma Distribution with Applications to Count Data." Journal of Current Science and Technology 15, no. 2 (2025): 102. https://doi.org/10.59796/jcst.v15n2.2025.102.

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This study proposes new estimators and confidence intervals for the population mean of the Poisson-Xgamma distribution, which are useful for overdispersed count data analysis. We prove that the proposed estimators using maximum likelihood and method of moments estimation are consistent and establish the variance of the estimators. Moreover, the confidence intervals are constructed based on large-sample theory and bootstrap method. The former method utilizes the properties of the maximum likelihood and moment estimators, the likelihood ratio, and the asymptotic normality property of the log-tra

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Farghali, Rasha A., Adewale F. Lukman, Zakariya Algamal, Murat Genc, and Hend Attia. "An Alternative Estimator for Poisson–Inverse-Gaussian Regression: The Modified Kibria–Lukman Estimator." Algorithms 18, no. 3 (2025): 169. https://doi.org/10.3390/a18030169.

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Poisson regression is used to model count response variables. The method has a strict assumption that the mean and variance of the response variable are equal, while, in practice, the case of overdispersion is common. Also, in multicollinearity, the model parameter estimates obtained with the maximum likelihood estimator are adversely affected. This paper introduces a new biased estimator that extends the modified Kibria–Lukman estimator to the Poisson–Inverse-Gaussian regression model to deal with overdispersion and multicollinearity in the data. The superiority of the proposed estimator over

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Aladeitan, Benedicta B., Olukayode Adebimpe, Adewale F. Lukman, Olajumoke Oludoun, and Oluwakemi E. Abiodun. "Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation." F1000Research 10 (December 14, 2021): 548. http://dx.doi.org/10.12688/f1000research.53987.2.

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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 Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity. Methods: A simulation study and a real-life study was carried out and the performance of the new estimator was compared with some of t

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Aladeitan, Benedicta B., Olukayode Adebimpe, Adewale F. Lukman, Olajumoke Oludoun, and Oluwakemi E. Abiodun. "Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation." F1000Research 10 (July 8, 2021): 548. http://dx.doi.org/10.12688/f1000research.53987.1.

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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 Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity. Methods: A simulation study and a real-life study were carried out and the performance of the new estimator was compared with some of

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Jumamyradov, Maksat, Murat Munkin, William H. Greene, and Benjamin M. Craig. "Biases in the Maximum Simulated Likelihood Estimation of the Mixed Logit Model." Econometrics 12, no. 2 (2024): 8. http://dx.doi.org/10.3390/econometrics12020008.

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In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated

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32

Mitnik, Pablo A. "Intergenerational Income Elasticities, Instrumental Variable Estimation, and Bracketing Strategies." Sociological Methodology 50, no. 1 (2020): 1–46. http://dx.doi.org/10.1177/0081175019887992.

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The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than po

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Algamal, Zakariya Yahya, Adewale F. Lukman, Mohamed R. Abonazel, and Fuad A. Awwad. "Performance of the Ridge and Liu Estimators in the zero-inflated Bell Regression Model." Journal of Mathematics 2022 (September 13, 2022): 1–15. http://dx.doi.org/10.1155/2022/9503460.

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The Poisson regression model is popularly used to model count data. However, the model suffers drawbacks when there is overdispersion—when the mean of the Poisson distribution is not the same as the variance. In this situation, the Bell regression model fits well to the data. Also, there is a high tendency of excess zeros in the count data. In this case, the zero-inflated Bell regression model is an alternative to the Bell regression model. The parameters of the zero-inflated Bell regression model are mostly estimated using the method of maximum likelihood. Linear dependency is a threat in a r

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Shukla, Kamlesh Kumar, and Rama Shanker. "On statistical properties and applications of PoissonPranav distribution." Biometrics & Biostatistics International Journal 11, no. 3 (2022): 93–98. http://dx.doi.org/10.15406/bbij.2022.11.00360.

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In this paper, Poisson-Pranav distribution, a Poisson mixture of Pranav distribution, has been proposed. Its moments and moments-based measures including coefficients of variation, skewness, kurtosis, index of dispersion have been obtained and their behavior illustrated graphically. The estimation of parameter of the proposed distribution has been discussed using both the method of moment and the method of maximum likelihood. The simulation study has also been presented in order to illustrate the performance of maximum likelihood estimator. The goodness of fit of the proposed distribution has

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Peci, Jurgen, and Ana Isabel Sanjuán. "The dual trade impact of non-tariff measures: an empirical assessment of China’s pork imports." European Review of Agricultural Economics 47, no. 5 (2020): 1716–39. http://dx.doi.org/10.1093/erae/jbaa005.

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Abstract Theoretical arguments and empirical evidence suggest that non-tariff measures (NTMs) generate a mixture of trade effects. Using the maximum level of disaggregation provided by the UNCTAD NTMs database (four-digit), and focusing on those measures applied by China in its pork trade, a gravity equation is estimated with the poisson pseudo maximum likelihood estimator. Results confirm both restricting and promoting effects that higher levels of NTM aggregation mask. Compared to the average tariff applied by main importers (9 per cent) and China (14 per cent), the most restrictive NTMs are

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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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Diehn, Manuel, Axel Munk, and Daniel Rudolf. "Maximum likelihood estimation in hidden Markov models with inhomogeneous noise." ESAIM: Probability and Statistics 23 (2019): 492–523. http://dx.doi.org/10.1051/ps/2018017.

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We consider parameter estimation in finite hidden state space Markov models with time-dependent inhomogeneous noise, where the inhomogeneity vanishes sufficiently fast. Based on the concept of asymptotic mean stationary processes we prove that the maximum likelihood and a quasi-maximum likelihood estimator (QMLE) are strongly consistent. The computation of the QMLE ignores the inhomogeneity, hence, is much simpler and robust. The theory is motivated by an example from biophysics and applied to a Poisson- and linear Gaussian model.

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Kim, Byungsoo, Sangyeol Lee, and Dongwon Kim. "Robust Estimation for Bivariate Poisson INGARCH Models." Entropy 23, no. 3 (2021): 367. http://dx.doi.org/10.3390/e23030367.

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In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of

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Ibrahim, Shuaib Mursal, and Aydın Karakoca. "Modified Ridge Estimator for Poisson Regression." Cumhuriyet Science Journal 45, no. 4 (2024): 811–22. https://doi.org/10.17776/csj.1372265.

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Poisson 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 when the independent variables are highly correlated with each other. Multicollinearity can affect the maximum likelihood (ML) estimates of unknown model parameters, making them unstable and inaccurate. In this study, we propose a modified ridge parameter estimator to combat multicollinearity in Poisson regression. We conducted extensive simul

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Green, Nick, and J. M. C. Santos Silva. "A cautionary note on goodness-of-fit statistics for models estimated by pseudo maximum likelihood." Stata Journal: Promoting communications on statistics and Stata 25, no. 2 (2025): 458–65. https://doi.org/10.1177/1536867x251341411.

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We argue that measures of goodness of fit based on the value of the likelihood function should not be used when models are estimated by pseudo maximum likelihood. We illustrate this point by showing that when the dependent variable is not a count, some measures of goodness of fit for Poisson regression routinely reported by Stata commands depend on the scale of the data and are therefore uninformative.

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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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Dadakas, Dimitrios, Salim Ghazvini Kor, and Scott Fargher. "Examining the trade potential of the UAE using a gravity model and a Poisson pseudo maximum likelihood estimator." Journal of International Trade & Economic Development 29, no. 5 (2020): 619–46. http://dx.doi.org/10.1080/09638199.2019.1710551.

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Truong, Hoan Quang, Chung Van Dong, Huong Thu Thi Nguyen, and Hung Xuan Nguyen. "Determinants of aggregate and sectoral trade between Japan and ASEAN countries: a Poisson pseudo-maximum-likelihood estimator approach." Global Business and Economics Review 32, no. 4 (2025): 418–40. https://doi.org/10.1504/gber.2025.146496.

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Dean, C. B. "Modified pseudo-likelihood estimator of the overdispersion parameter in Poisson mixture models." Journal of Applied Statistics 21, no. 6 (1994): 523–32. http://dx.doi.org/10.1080/757584213.

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Ristanto, Didi. "Indonesia’s Potential Trade Flow to EAEU Countries: The Gravity Model Approach." Jambura Equilibrium Journal 6, no. 2 (2024): 87–95. http://dx.doi.org/10.37479/jej.v6i2.24981.

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This research aims to examine the Indonesia’s potential trade flow to EAEU countries using Augmented Gravity Model with Pseudo Poisson Maximum Likelihood (PPML) estimator, revealed comparative advantage (RCA) index, and constant market share analysis (CMSA) index on trade flows data for the period 2002-2022. The results indicates that trade relation between Indonesia and EAEU countries align with Heckscher-Ohlin Theory of trade, which states that dissimilar countries tend to trade more. Furthermore, the finding emphasizes the importance of Indonesia-EAEU preferential trade agreement to reduce

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Santiago, Nymphmary Daphne J., and Ma. Salome B. Bulayog. "Estimation of the Recreational Value of Tourist Destinations in Camotes Islands using Travel Cost Method." Review of Socio-Economic Research and Development Studies 3, no. 1 (2019): 19–37. https://doi.org/10.5281/zenodo.4518592.

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Determining the recreational value of tourist destinations is important because natural resources provide services that possess non-market values. They are considered as public goods, whereby society tends to overexploit and overconsume. In this regard, the valuation of four selected tourist destinations in Camotes Islands, Cebu, Philippines was determined. This study specifically aims to (1) estimate the demand function and consumer surplus of tourists, (2) determine the factors affecting the number of visits of tourists in every destination, (3) determine the tourists’ willingness to p

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Abdelwahab, Mahmoud M., Mohamed R. Abonazel, Ali T. Hammad, and Amera M. El-Masry. "Modified Two-Parameter Liu Estimator for Addressing Multicollinearity in the Poisson Regression Model." Axioms 13, no. 1 (2024): 46. http://dx.doi.org/10.3390/axioms13010046.

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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 are correlated, thus leading to multicollinearity, the variance or standard error of the PMLE is inflated. To address this issue, several alternative estimators have been introduced, including the Poisson ridge regression estimator (PRRE), Liu estimator (PLE), and adjusted Liu estimator (PALE), ea

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Al-Juboori, F. S. "A new method of Poisson regression estimator in the presence of a Multicollinearity problem: Simulation and application." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 2 (2022): 164–88. https://doi.org/10.21533/pen.v10.i2.591.

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A new estimator for the Poisson model is introduced in this study. Poisson regression model is an important log- Linear models which is the tool of modeling the dependent variable when its values are positive and as a form of count data or rates additional to be the appropriate model for analyzing rare events. The maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). The purpose of this study is to make a comparison of parameters estimation methods for the Poisson regression model when that model suffe

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Pawlas, Zbynek, and Marketa Zikmundova. "A COMPARISON OF NONPARAMETRIC ESTIMATORS FOR LENGTH DISTRIBUTION IN LINE SEGMENT PROCESSES." Image Analysis & Stereology 38, no. 2 (2019): 121. http://dx.doi.org/10.5566/ias.1889.

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We study nonparametric estimation of the length distribution for stationary line segment processes in the d-dimensional Euclidean space. Several methods have been proposed in the literature. We review different approaches (Horvitz-Thompson type estimator, reduced-sample estimator, Kaplan-Meier estimator, nonparametric maximum likelihood estimator, stochastic restoration estimation) and compare the finite sample behaviour by means of a simulation study for stationary line segment processes in 2D and 3D. Several data generating processes (Poisson point process, Matérn cluster process and Matérn

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Alreshidi, Nasser A., Masad A. Alrasheedi, Adewale F. Lukman, Hleil Alrweili, and Rasha A. Farghali. "Modified Kibria–Lukman Estimator for the Conway–Maxwell–Poisson Regression Model: Simulation and Application." Mathematics 13, no. 5 (2025): 794. https://doi.org/10.3390/math13050794.

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This study presents a novel estimator that combines the Kibria–Lukman and ridge estimators to address the challenges of multicollinearity in Conway–Maxwell–Poisson (COMP) regression models. The Conventional COMP Maximum Likelihood Estimator (CMLE) is notably susceptible to the adverse effects of multicollinearity, underscoring the necessity for alternative estimation strategies. We comprehensively compare the proposed COMP Modified Kibria–Lukman estimator (CMKLE) against existing methodologies to mitigate multicollinearity effects. Through rigorous Monte Carlo simulations and real-world applic

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