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Modeling Score Distributions and Continuous Covariates: A Bayesian Approach [article]

Mel McCurrie, Hamish Nicholson, Walter J. Scheirer, Samuel Anthony
2020 arXiv   pre-print
We develop a generative model of the match and non-match score distributions over continuous covariates and perform inference with modern Bayesian methods.  ...  In biometric verification, model performance over continuous covariates---real-number attributes of images that affect performance---is particularly challenging to study.  ...  Instead of directly modeling the final performance metric, we model the underlying match and non-match distributions of feature distances over continuous covariates with flexible Bayesian methods.  ... 
arXiv:2009.09583v1 fatcat:s4g3ni2ycfcftbdd237xk5bsca

GPMatch: A Bayesian Doubly Robust Approach to Causal Inference with Gaussian Process Covariance Function As a Matching Tool [article]

Bin Huang, Chen Chen, Jinzhong Liu
2019 arXiv   pre-print
Gaussian process (GP) covariance function is proposed as a matching tool in GPMatch within a full Bayesian framework under relatively weaker causal assumptions.  ...  The results demonstrate that GPMatch enjoys well calibrated frequentist properties, and outperforms many widely used methods including Bayesian Additive Regression Trees.  ...  First, we offer a principled approach to Bayesian causal inference utilizing GP prior covariance function as a matching tool, which accomplishes matching and flexible outcome modeling in a single step.  ... 
arXiv:1901.10359v2 fatcat:p5dl6f7y6bcprdku5kkziwalbq

Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data

J. T. Gaskins, M. J. Daniels
2016 Journal of Computational And Graphical Statistics  
Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain  ...  This approach additionally encourages a lowerdimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero.  ...  Acknowledgments This research was partially supported by NIH grants CA-85295 and CA-183854.  ... 
doi:10.1080/10618600.2015.1028549 pmid:27175055 pmcid:PMC4861405 fatcat:2s6p6ugkxzgdbaahcvtch2hzrm

A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates

Nanhua Zhang, Roderick J. Little
2011 Biometrics  
We propose a pseudo-Bayesian approach for regression with missing covariates that compromises between the CC and DV estimates, exploiting information in the incomplete cases when the data support DV assumptions  ...  likelihood based on the observed data, assuming the missing data are missing at random (Rubin, 1976b) , and (iii) nonignorable modeling, which posits a joint distribution of the variables and missing  ...  Acknowledgements We thank the associate editor and referees for helpful comments. Dr Little's research was partially supported by an Inter-Personnel Agreement with the U.S. Census Bureau.  ... 
doi:10.1111/j.1541-0420.2011.01718.x pmid:22150765 fatcat:2v44sh5ofvhk7lpivmztsfqwrm

Identifying prognosticators covariates of child nutritional status in ethiopia: A bayesian generalized additive modelling approach

Reta Habtamu Bacha
2020 Biometrics & Biostatistics International Journal  
Bayesian generalized additive regression model applied to flexibly estimate effects of socio-economic, demographic, health and environmental covariates.  ...  This study aimed to figure out determinants of Ethiopian children malnutrition by applying Bayesian approach with Markov chain Monte Carlo (MCMC) techniques on the 2011 EDHS data.  ...  A closely related approach for continuous covariates is based on the P-splines approach introduced by Eilers and Marx (1996) .  ... 
doi:10.15406/bbij.2020.09.00297 fatcat:6llox7fvbzgnrojcoukbqqxd2y

A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit

Chen
2014 International Journal of Statistics in Medical Research  
In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs.  ...  We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the  ...  ACKNOWLEDGEMENTS The authors thank the editor and referees for helpful comments. The work received support from National Institutes of Health (R21HL097334, ULI RR024975-01, HL081332).  ... 
doi:10.6000/1929-6029.2014.03.01.5 pmid:24772198 pmcid:PMC3998726 fatcat:t5lsdzplpjfblku5p33px5cuye

A Bayesian approach to the g-formula

Alexander P Keil, Eric J Daza, Stephanie M Engel, Jessie P Buckley, Jessie K Edwards
2017 Statistical Methods in Medical Research  
We adopt the language of potential outcomes under Rubin's original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach.  ...  We give a general algorithm and supply SAS and Stan code that can be adopted to implement our computational approach in both time-fixed and longitudinal data.  ...  (for whom the prior is a complex function of the model parameters and target population covariate distribution).  ... 
doi:10.1177/0962280217694665 pmid:29298607 pmcid:PMC5790647 fatcat:jyokvdnnnfdqhl2aefcwxjtgw4

Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions From Coarsened Data

Sean F. Reardon, Benjamin R. Shear, Katherine E. Castellano, Andrew D. Ho
2016 Journal of educational and behavioral statistics  
We demonstrate and evaluate this novel application of the HETOP model with a simulation study and using real test score data from two sources.  ...  We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups' test score distributions from such data.  ...  In this article, we describe an approach that allows the analyst to recover more complete information about continuous test score distributions when only coarsened test score data are available.  ... 
doi:10.3102/1076998616666279 fatcat:2lv7oulzgbeupgt5zmuh2maqqu

Adjustment for Baseline Covariates to Increase Efficiency in RCTs with Binary Endpoint: A Comparison of Bayesian and Frequentist Approaches

Paola Berchialla, Veronica Sciannameo, Sara Urru, Corrado Lanera, Danila Azzolina, Dario Gregori, Ileana Baldi
2021 International Journal of Environmental Research and Public Health  
the semiparametric locally efficient estimator) and Bayesian (model averaging), adjustment for confounding, and generalized Bayesian causal effect estimation frameworks are assessed and compared in a  ...  The Bayesian estimators showed a higher type II error than frequentist estimators if noisy covariates are included in the treatment model.  ...  This study used secondary data already collected and publicly available online.  ... 
doi:10.3390/ijerph18157758 fatcat:shhpcd4obbczpbf3mxjy3aizqm

Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization [article]

Sébastien J Petit
2022 arXiv   pre-print
It can be viewed as a goal-oriented method and becomes particularly interesting in Bayesian optimization, for example, for the minimization of an objective function, where good predictive distributions  ...  This work presents a new procedure for obtaining predictive distributions in the context of Gaussian process (GP) modeling, with a relaxation of the interpolation constraints outside some ranges of interest  ...  A standard Bayesian approach to this problem consists in using a GP model ξ ∼ GP (µ, k) as a prior about f , where µ : X → R is a mean function and k : X × X → R is a covariance function, which is supposed  ... 
arXiv:2206.03034v2 fatcat:57iq34waovfupfxg7u6z2wqxp4

bayesQR: A Bayesian Approach to Quantile Regression

Dries F. Benoit, Dirk Van den Poel
2017 Journal of Statistical Software  
The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution.  ...  After its introduction by Koenker and Basset (1978) , quantile regression has become an important and popular tool to investigate the conditional response distribution in regression.  ...  The package contains a set of functions that estimate different types of quantile regression models using a Bayesian approach.  ... 
doi:10.18637/jss.v076.i07 fatcat:b7ht4ycdlfdzndyeio3qcjcrai

Swipe dynamics as a means of authentication: results from a Bayesian unsupervised approach [article]

Parker Lamb, Alexander Millar, Ramon Fuentes
2020 arXiv   pre-print
Three models are compared using this dataset; two single-mode models: a shrunk covariance estimate and a Bayesian Gaussian distribution, as well as a Bayesian non-parametric infinite mixture of Gaussians  ...  From a machine learning perspective, this presents the classic curse of dimensionality problem and the methodology presented here focuses on Bayesian unsupervised models as they are well suited to such  ...  The Bayesian Gaussian model has arguably the best distribution of scores in this scenario, with a median EER of 5.96%.  ... 
arXiv:2008.01013v2 fatcat:lpvl3znd55ey3abtamnry6g6na

High-dimensional Multivariate Geostatistics: A Bayesian Matrix-Normal Approach [article]

Lu Zhang, Sudipto Banerjee, Andrew O. Finley
2020 arXiv   pre-print
We discuss differences between modeling the multivariate response itself as a spatial process and that of modeling a latent process in a hierarchical model.  ...  This manuscript develops a conjugate Bayesian framework for analyzing multivariate spatial data using analytically tractable posterior distributions that obviate iterative algorithms.  ...  The third author was supported by NSF/EF 1253225 and NSF/DMS 1916395, and National Aeronautics and Space Administration's Carbon Monitoring System project.  ... 
arXiv:2003.10051v5 fatcat:bviiwqau35amlefbpljr7jh7cm

A Novel Algorithmic Approach to Bayesian Logic Regression

Aliaksandr Hubin, Geir Storvik, Florian Frommlet
2018 Bayesian Analysis  
Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates.  ...  Here we will adopt an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models  ...  Appendix A. Supplementary data Supplementary material related to this article can be found online at https://doi.org/10.1016/j.csda.2018.05.020.  ... 
doi:10.1214/18-ba1141 fatcat:nmkgzaxnc5c4fcle4q5d6lqdca

A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves [article]

Yanbo Xu, Yanxun Xu, Suchi Saria
2016 arXiv   pre-print
We leverage the G-computation formula and develop a novel Bayesian nonparametric (BNP) method that can flexibly model functional data and provide posterior inference over the treatment response curves  ...  than alternative approaches.  ...  (2000) ), we propose below a Bayesian nonparametric model that models longitudinal responses in three parts: a baseline progression with no treatments prescribed, responses to treatments over time, and  ... 
arXiv:1608.05182v2 fatcat:jqq2r2id6vc2bltyrqzwrdmara
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