A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Modeling Score Distributions and Continuous Covariates: A Bayesian Approach
[article]
2020
arXiv
pre-print
Preserved Fulltext
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]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2016
Journal of Computational And Graphical Statistics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2011
Biometrics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2020
Biometrics & Biostatistics International Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2014
International Journal of Statistics in Medical Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2017
Statistical Methods in Medical Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2016
Journal of educational and behavioral statistics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2021
International Journal of Environmental Research and Public Health
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2017
Journal of Statistical Software
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2008.01013v1 fatcat:d2bui3set5dirpic6roelucade
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2018
Bayesian Analysis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
« Previous
Showing results 1 — 15 out of 45,047 results