The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Daniel S Nagin - One of the best experts on this subject based on the ideXlab platform.
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a comparison of Poisson negative binomial and semiparametric mixed Poisson Regression models with empirical applications to criminal careers data
Sociological Methods & Research, 1996Co-Authors: Kenneth C Land, Patricia L Mccall, Daniel S NaginAbstract:Specifications and moment properties of the univariate Poisson and negative binomial distributions are briefly reviewed and illustrated. Properties and limitations of the corresponding Poisson and negative binomial (gamma mixtures of Poissons) Regression models are described. It is shown how a misspecification of the mixing distribution of a mixed Poisson model to accommodate hidden heterogeneity ascribable to unobserved variables—although not affecting the consistency of maximum likelihood estimators of the Poisson mean rate parameter or its Regression parameterization—can lead to inflated t ratios of Regression coefficients and associated incorrect inferences. Then the recently developed semiparametric maximum likelihood estimator for Regression models composed of arbitrary mixtures of Poisson processes is specified and further developed. It is concluded that the semiparametric mixed Poisson Regression model adds considerable flexibility to Poisson-family Regression models and provides opportunities for...
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a comparison of Poisson negative binomial and semiparametric mixed Poisson Regression models with empirical applications to criminal careers data
Sociological Methods & Research, 1996Co-Authors: Kenneth C Land, Patricia L Mccall, Daniel S NaginAbstract:Specifications and moment properties of the univariate Poisson and negative binomial distributions are briefly reviewed and illustrated. Properties and limitations of the corresponding Poisson and negative binomial (gamma mixtures of Poissons) Regression models are described. It is shown how a misspecification of the mixing distribution of a mixed Poisson model to accommodate hidden heterogeneity ascribable to unobserved variables—although not affecting the consistency of maximum likelihood estimators of the Poisson mean rate parameter or its Regression parameterization—can lead to inflated t ratios of Regression coefficients and associated incorrect inferences. Then the recently developed semiparametric maximum likelihood estimator for Regression models composed of arbitrary mixtures of Poisson processes is specified and further developed. It is concluded that the semiparametric mixed Poisson Regression model adds considerable flexibility to Poisson-family Regression models and provides opportunities for interpretation of empirical patterns not available in the conventional approaches.
Meredith Franklin - One of the best experts on this subject based on the ideXlab platform.
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comparing performance between log binomial and robust Poisson Regression models for estimating risk ratios under model misspecification
BMC Medical Research Methodology, 2018Co-Authors: Wansu Chen, Lei Qian, Jiaxiao Shi, Meredith FranklinAbstract:Background Log-binomial and robust (modified) Poisson Regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood.
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comparing performance between log binomial and robust Poisson Regression models for estimating risk ratios under model misspecification
BMC Medical Research Methodology, 2018Co-Authors: Wansu Chen, Lei Qian, Jiaxiao Shi, Meredith FranklinAbstract:Log-binomial and robust (modified) Poisson Regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios.
Guangyong Zou - One of the best experts on this subject based on the ideXlab platform.
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extension of the modified Poisson Regression model to prospective studies with correlated binary data
Statistical Methods in Medical Research, 2013Co-Authors: Guangyong Zou, Allan DonnerAbstract:The Poisson Regression model using a sandwich variance estimator has become a viable alternative to the logistic Regression model for the analysis of prospective studies with independent binary outcomes. The primary advantage of this approach is that it readily provides covariate-adjusted risk ratios and associated standard errors. In this article, the model is extended to studies with correlated binary outcomes as arise in longitudinal or cluster randomization studies. The key step involves a cluster-level grouping strategy for the computation of the middle term in the sandwich estimator. For a single binary exposure variable without covariate adjustment, this approach results in risk ratio estimates and standard errors that are identical to those found in the survey sampling literature. Simulation results suggest that it is reliable for studies with correlated binary data, provided the total number of clusters is at least 50. Data from observational and cluster randomized studies are used to illustrate the methods.
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a modified Poisson Regression approach to prospective studies with binary data
American Journal of Epidemiology, 2004Co-Authors: Guangyong ZouAbstract:Abstract Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson Regression approach (i.e., Poisson Regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
Kenneth C Land - One of the best experts on this subject based on the ideXlab platform.
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a comparison of Poisson negative binomial and semiparametric mixed Poisson Regression models with empirical applications to criminal careers data
Sociological Methods & Research, 1996Co-Authors: Kenneth C Land, Patricia L Mccall, Daniel S NaginAbstract:Specifications and moment properties of the univariate Poisson and negative binomial distributions are briefly reviewed and illustrated. Properties and limitations of the corresponding Poisson and negative binomial (gamma mixtures of Poissons) Regression models are described. It is shown how a misspecification of the mixing distribution of a mixed Poisson model to accommodate hidden heterogeneity ascribable to unobserved variables—although not affecting the consistency of maximum likelihood estimators of the Poisson mean rate parameter or its Regression parameterization—can lead to inflated t ratios of Regression coefficients and associated incorrect inferences. Then the recently developed semiparametric maximum likelihood estimator for Regression models composed of arbitrary mixtures of Poisson processes is specified and further developed. It is concluded that the semiparametric mixed Poisson Regression model adds considerable flexibility to Poisson-family Regression models and provides opportunities for...
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a comparison of Poisson negative binomial and semiparametric mixed Poisson Regression models with empirical applications to criminal careers data
Sociological Methods & Research, 1996Co-Authors: Kenneth C Land, Patricia L Mccall, Daniel S NaginAbstract:Specifications and moment properties of the univariate Poisson and negative binomial distributions are briefly reviewed and illustrated. Properties and limitations of the corresponding Poisson and negative binomial (gamma mixtures of Poissons) Regression models are described. It is shown how a misspecification of the mixing distribution of a mixed Poisson model to accommodate hidden heterogeneity ascribable to unobserved variables—although not affecting the consistency of maximum likelihood estimators of the Poisson mean rate parameter or its Regression parameterization—can lead to inflated t ratios of Regression coefficients and associated incorrect inferences. Then the recently developed semiparametric maximum likelihood estimator for Regression models composed of arbitrary mixtures of Poisson processes is specified and further developed. It is concluded that the semiparametric mixed Poisson Regression model adds considerable flexibility to Poisson-family Regression models and provides opportunities for interpretation of empirical patterns not available in the conventional approaches.
Zhongren Peng - One of the best experts on this subject based on the ideXlab platform.
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exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson Regression
Journal of Transport Geography, 2019Co-Authors: Zhongren PengAbstract:Abstract Ridesourcing, or on-demand ridesharing, is quickly changing today's travel. Recently, research has linked socio-demographics to ridesourcing use. However, little of the research has focused on the impacts of built environment, an important factor to consider in understanding travel behavior. This study applied Geographically Weighted Poisson Regression (GWPR) and examined the spatial relationships between built environment and ridesourcing demand. We used 2016–2017 ridesourcing trip data from a transportation network company (TNC), RideAustin, in Austin, Texas. By capturing the spatial heterogeneity, the GWPRs considerably improve modeling fit compared to the global models. Modeling results present strong relationships between ridesourcing demand and built environment variables (i.e., density, land use, infrastructure, and transit accessibility). More importantly, the results demonstrate significant spatial variations of the effects of both built environment and socio-economic variables and geographic trends from urban to suburban neighborhoods. Overall, these findings suggest that built environment factors have significant impacts on ridesourcing demand, and it is important to consider the spatial context. The study provides useful insights for understanding ridesourcing use as a function of built environment and have important implications for transportation planning, demand modeling, and urban governance.
