scholarly journals A hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion: A simulation study

2013 ◽  
Vol 57 (1) ◽  
pp. 233-245 ◽  
Author(s):  
Mehreteab Aregay ◽  
Ziv Shkedy ◽  
Geert Molenberghs
Keyword(s):  
Bayesian Approach ◽  
Simulation Study ◽  
Count Data ◽  
Hierarchical Bayesian ◽  
Longitudinal Count Data

scholarly journals A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists

PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e26785 ◽  
Author(s):  
James A. Fordyce ◽  
Zachariah Gompert ◽  
Matthew L. Forister ◽  
Chris C. Nice
Keyword(s):  
Bayesian Approach ◽  
Count Data ◽  
Hierarchical Bayesian ◽  
Flexible Tool

A comparison of salmon escapement estimates using a hierarchical Bayesian approach versus separate maximum likelihood estimation of each year's return

2001 ◽  
Vol 58 (8) ◽  
pp. 1663-1671 ◽  
Author(s):  
Milo D Adkison ◽  
Zhenming Su
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Bayesian Approach ◽  
Count Data ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Hierarchical Bayesian ◽  
Hierarchical Approach ◽  
Bayesian Hierarchical ◽  
The Bayesian Approach

In this simulation study, we compared the performance of a hierarchical Bayesian approach for estimating salmon escapement from count data with that of separate maximum likelihood estimation of each year's escapement. We simulated several contrasting counting schedules resulting in data sets that differed in information content. In particular, we were interested in the ability of the Bayesian approach to estimate escapement and timing in years where few or no counts are made after the peak of escapement. We found that the Bayesian hierarchical approach was much better able to estimate escapement and escapement timing in these situations. Separate estimates for such years could be wildly inaccurate. However, even a single postpeak count could dramatically improve the estimability of escapement parameters.

scholarly journals Population size estimation based upon zero-truncated, one-inflated and sparse count data

2021 ◽  
Author(s):  
Dankmar Böhning ◽  
Herwig Friedl
Keyword(s):  
Confidence Intervals ◽  
Bayesian Approach ◽  
Simulation Study ◽  
Count Data ◽  
Parametric Bootstrap ◽  
Size Estimation ◽  
Jeffreys Prior ◽  
Likelihood Approach ◽  
Flare Stars ◽  
The Bayesian Approach

AbstractEstimating the size of a hard-to-count population is a challenging matter. In particular, when only few observations of the population to be estimated are available. The matter gets even more complex when one-inflation occurs. This situation is illustrated with the help of two examples: the size of a dice snake population in Graz (Austria) and the number of flare stars in the Pleiades. The paper discusses how one-inflation can be easily handled in likelihood approaches and also discusses how variances and confidence intervals can be obtained by means of a semi-parametric bootstrap. A Bayesian approach is mentioned as well and all approaches result in similar estimates of the hidden size of the population. Finally, a simulation study is provided which shows that the unconditional likelihood approach as well as the Bayesian approach using Jeffreys’ prior perform favorable.

scholarly journals A Bayesian approach to analyse overdispersed longitudinal count data

2015 ◽  
Vol 43 (11) ◽  
pp. 2085-2109
Author(s):  
Fernanda B. Rizzato ◽  
Roseli A. Leandro ◽  
Clarice G.B. Demétrio ◽  
Geert Molenberghs
Keyword(s):  
Bayesian Approach ◽  
Count Data ◽  
Longitudinal Count Data

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

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