Bayesian estimation and model comparison for linear dynamic panel models with missing values

Aßmann, Christian; Preising, Marcel

doi:10.1111/anzs.12316

Cited by 3 publications

(2 citation statements)

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“…involved in Bayes factors to allow for non-nested model comparison is possible along the lines suggested by Chib (1995), Chib and Jeliazkov (2001) and Aßmann and Preising (2020) in the context of linear dynamic panel models.…”

Section: Cor(θmentioning

confidence: 99%

A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models

2022

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The measurement of latent traits and investigation of relations between these and a potentially large set of explaining variables is typical in psychology, economics, and the social sciences. Corresponding analysis often relies on surveyed data from large-scale studies involving hierarchical structures and missing values in the set of considered covariates. This paper proposes a Bayesian estimation approach based on the device of data augmentation that addresses the handling of missing values in multilevel latent regression models. Population heterogeneity is modeled via multiple groups enriched with random intercepts. Bayesian estimation is implemented in terms of a Markov chain Monte Carlo sampling approach. To handle missing values, the sampling scheme is augmented to incorporate sampling from the full conditional distributions of missing values. We suggest to model the full conditional distributions of missing values in terms of non-parametric classification and regression trees. This offers the possibility to consider information from latent quantities functioning as sufficient statistics. A simulation study reveals that this Bayesian approach provides valid inference and outperforms complete cases analysis and multiple imputation in terms of statistical efficiency and computation time involved. An empirical illustration using data on mathematical competencies demonstrates the usefulness of the suggested approach.

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Section: Cor(θmentioning

confidence: 99%

A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models

2022

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“…There are generally two ways to deal with missing values [ 17 ]: When the continuous deletion period of the feature sequence is too long or the number of missing values reaches more than 50% of the total period length, the feature sequence is directly removed. When there are scattered missing values, an interpolation method is used to make up.…”

Section: Research On Runoff Driving Factor Mining Based On Big Data Analysismentioning

confidence: 99%

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Ting

2021

Computational Intelligence and Neuroscience

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Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China.

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Automated Bayesian variable selection methods for binary regression models with missing covariate data

Bergrab,

Aßmann

2024

AStA Wirtsch Sozialstat Arch

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Data collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression modeling, as it helps to identify an appropriate model with respect to the considered set of conditioning variables. Second, especially in the context of survey data, handling of missing values is important for estimation, which occur even with state-of-the-art data collection and processing methods. Within this paper, we provide an Bayesian approach based on a spike-and-slab prior for the regression coefficients, which allows for simultaneous handling of variable selection and estimation in combination with handling of missing values in covariate data. The paper also discusses the implementation of the approach using Markov chain Monte Carlo techniques and provides results for simulated data sets and an empirical illustration based on data from the German National Educational Panel Study. The suggested Bayesian approach is compared to other statistical and machine learning frameworks such as Lasso, ridge regression, and Elastic net, and is shown to perform well in terms of estimation performance and variable selection accuracy. The simulation results demonstrate that ignoring the handling of missing values in data sets can lead to the generation of biased selection results. Overall, the proposed Bayesian method offers a holistic, flexible, and powerful framework for variable selection in the presence of missing covariate data.

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Bayesian estimation and model comparison for linear dynamic panel models with missing values

Cited by 3 publications

References 50 publications

A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models

A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models

Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Automated Bayesian variable selection methods for binary regression models with missing covariate data

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