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1. Approximate Bayesian inference under informative sampling

   https://doi.org/10.1093/biomet/asx073  

   Wang, Z ; Kim, J K ; Yang, S ( December 2017 , Biometrika)
2. Small‐sample inference for cluster‐based outcome‐dependent sampling schemes in resource‐limited settings: Investigating low birthweight in Rwanda

   https://doi.org/10.1111/biom.13423  

   Sauer, Sara ; Hedt‐Gauthier, Bethany ; Rivera‐Rodriguez, Claudia ; Haneuse, Sebastien ( January 2021 , Biometrics)

   The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1000 live births. As part of a larger effort to reduce preventable neonatal deaths in the country, we conducted a study to examine risk factors for low birthweight. The data were collected via a cost‐efficient cluster‐based outcome‐dependent sampling (ODS) scheme wherein clusters of individuals (health centers) were selected on the basis of, in part, the outcome rate of the individuals. For a given data set collected via a cluster‐based ODS scheme, estimation for a marginal model may proceed via inverse‐probability‐weighted generalized estimating equations, where the cluster‐specific weights are the inverse probability of the health center's inclusion in the sample. In this paper, we provide a detailed treatment of the asymptotic properties of this estimator, together with an explicit expression for the asymptotic variance and a corresponding estimator. Furthermore, motivated by the study we conducted in Rwanda, we propose a number of small‐sample bias corrections to both the point estimates and the standard error estimates. Through simulation, we show that applying these corrections when the number of clusters is small generally reduces the bias in the point estimates, and results in closer to nominal coverage. The proposed methods are applied to data from 18 health centers and 1 district hospital in Rwanda.

    

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3. Sampling, variational Bayesian inference, and conditioned stochastic differential equations

   https://doi.org/10.1109/CDC45484.2021.9683159  

   Coleman, Todd P. ; Raginsky, Maxim ( December 2021 , 60th IEEE Conference on Decision and Control (CDC))
4. Inference of Protein-Protein Interaction Networks from Liquid-Chromatography Mass-Spectrometry Data by Approximate Bayesian Computation-Sequential Monte Carlo Sampling

   https://doi.org/10.1109/MLSP49062.2020.9231824  

   Tan, Yukun ; Lima Neto, Fernando B. ; Neto, Ulisses Braga ( September 2020 , 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP))

   null (Ed.)

   We propose a new algorithm for inference of protein-protein interaction (PPI) networks from noisy time series of Liquid- Chromatography Mass-Spectrometry (LC-MS) proteomic expression data based on Approximate Bayesian Computation - Sequential Monte Carlo sampling (ABC-SMC). The algorithm is an extension of our previous framework PALLAS. The proposed algorithm can be easily modified to handle other complex models of expression data, such as LC-MS data, for which the likelihood function is intractable. Results based on synthetic time series of cytokine LC-MS measurements cor- responding to a prototype immunomic network demonstrate that our algorithm is capable of inferring the network topology accurately. 

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5. Computationally efficient Bayesian unit-level models for non-Gaussian data under informative sampling with application to estimation of health insurance coverage

   https://doi.org/10.1214/21-AOAS1524  

   Parker, Paul A. ; Holan, Scott H. ; Janicki, Ryan ( June 2022 , The Annals of Applied Statistics)