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Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing.

CITE Title : Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing. Personal Author(s) : Wheeler, Matthew W. Published Date : August 06 2018;3-2019; Source : Biometrics. 75(1):193-201 URL : https://stacks.cdc.gov/view/cdc/75828 Wheeler, Matthew W. (2018). Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing.. 75(1). Wheeler, Matthew W. "Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing." 75, no. 1 (2018) Wheeler, Matthew W. "Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing." vol. 75, no. 1, 2018 Export RIS Citation Information.

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Wheeler, Matthew W.

August 06 2018 | Biometrics. 75(1):193-201

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Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are e...
An Evaluation of Statistical Methods for Analyzing Follow-Up Gaussian Laboratory Data with a Lower Quantification Limit

CITE Title : An Evaluation of Statistical Methods for Analyzing Follow-Up Gaussian Laboratory Data with a Lower Quantification Limit Personal Author(s) : Karon, John M.;Wiegand, Ryan E.;van de Wijgert, Janneke H.;Kilmarx, Peter H.; Published Date : 2015;2015; Source : J Biopharm Stat. 25(4):812-829 URL : https://stacks.cdc.gov/view/cdc/83428 Karon, John M. et al. (2015). An Evaluation of Statistical Methods for Analyzing Follow-Up Gaussian Laboratory Data with a Lower Quantification Limit. 25(4). Karon, John M. et al. "An Evaluation of Statistical Methods for Analyzing Follow-Up Gaussian Laboratory Data with a Lower Quantification Limit" 25, no. 4 (2015) Karon, John M. et al. "An Evaluation of Statistical Methods for Analyzing Follow-Up Gaussian Laboratory Data with a Lower Quantification Limit" vol. 25, no. 4, 2015 Export RIS Citation Information.

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Karon, John M. ;

Wiegand, Ryan E.

2015 | J Biopharm Stat. 25(4):812-829

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Laboratory data with a lower quantification limit (censored data) are sometimes analyzed by replacing non-quantifiable values with a single value equa...
An empirical comparison of spatial scan statistics for outbreak detection

CITE Title : An empirical comparison of spatial scan statistics for outbreak detection Personal Author(s) : Neill, Daniel B Published Date : Apr 16 2009 Source : Int J Health Geogr. 2009; 8:20. URL : https://stacks.cdc.gov/view/cdc/22972 Neill, Daniel B (2009). An empirical comparison of spatial scan statistics for outbreak detection. 8 Neill, Daniel B "An empirical comparison of spatial scan statistics for outbreak detection" 8 (2009) Neill, Daniel B "An empirical comparison of spatial scan statistics for outbreak detection" vol. 8, 2009 Export RIS Citation Information.

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Neill, Daniel B

Apr 16 2009 | Int J Health Geogr. 2009; 8:20.

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BackgroundThe spatial scan statistic is a widely used statistical method for the automatic detection of disease clusters from syndromic data. Recent w...
Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies

CITE Title : Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies Personal Author(s) : Lin, Lin;Chan, Cliburn;Hadrup, Sine R.;Froesig, Thomas M.;Wang, Quanli;West, Mike; Published Date : Jun 2013 Source : Stat Appl Genet Mol Biol. 12(3):309-331. URL : https://stacks.cdc.gov/view/cdc/41981 Lin, Lin et al. (2013). Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies. 12(3). Lin, Lin et al. "Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies" 12, no. 3 (2013) Lin, Lin et al. "Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies" vol. 12, no. 3, 2013 Export RIS Citation Information.

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Lin, Lin ;

Chan, Cliburn

Jun 2013 | Stat Appl Genet Mol Biol. 12(3):309-331.

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Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing n...
Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit

CITE Title : Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit Personal Author(s) : Wiegand, Ryan E.;Rose, Charles E.;Karon, John M.; Published Date : May 05 2014 Source : Stat Methods Med Res. 25(6):2733-2749 URL : https://stacks.cdc.gov/view/cdc/83429 Wiegand, Ryan E. et al. (2014). Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit. 25(6). Wiegand, Ryan E. and Rose, Charles E. and Karon, John M. "Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit" 25, no. 6 (2014) Wiegand, Ryan E. et al. "Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit" vol. 25, no. 6, 2014 Export RIS Citation Information.

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Wiegand, Ryan E. ;

Rose, Charles E.

May 05 2014 | Stat Methods Med Res. 25(6):2733-2749

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A potential difficulty in the analysis of biomarker data occurs when data are subject to a detection limit. This detection limit is often defined as t...
Comparing GIS-Based Measures in Access to Mammography and Their Validity in Predicting Neighborhood Risk of Late-Stage Breast Cancer

CITE Title : Comparing GIS-Based Measures in Access to Mammography and Their Validity in Predicting Neighborhood Risk of Late-Stage Breast Cancer Personal Author(s) : Lian, Min;Struthers, James;Schootman, Mario; Published Date : Aug 28 2012 Source : PLoS One. 2012; 7(8). URL : https://stacks.cdc.gov/view/cdc/22725 Lian, Min et al. (2012). Comparing GIS-Based Measures in Access to Mammography and Their Validity in Predicting Neighborhood Risk of Late-Stage Breast Cancer. 7(8). Lian, Min and Struthers, James and Schootman, Mario "Comparing GIS-Based Measures in Access to Mammography and Their Validity in Predicting Neighborhood Risk of Late-Stage Breast Cancer" 7, no. 8 (2012) Lian, Min et al. "Comparing GIS-Based Measures in Access to Mammography and Their Validity in Predicting Neighborhood Risk of Late-Stage Breast Cancer" vol. 7, no. 8, 2012 Export RIS Citation Information.

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Lian, Min ;

Struthers, James

Aug 28 2012 | PLoS One. 2012; 7(8).

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BackgroundAssessing neighborhood environment in access to mammography remains a challenge when investigating its contextual effect on breast cancer-re...
GABA-edited MEGA-PRESS at 3T: Does a measured MM background improve linear combination modeling?

CITE Title : GABA-edited MEGA-PRESS at 3T: Does a measured MM background improve linear combination modeling? Personal Author(s) : Davies-Jenkins, Christopher W.;Zöllner, Helge J.;Simicic, Dunja;Hui, Steve C. N.;Song, Yulu;Hupfeld, Kathleen E.;Prisciandaro, James J.;Edden, Richard A.E.;Oeltzschner, Georg; Published Date : 10 2024;10-2024; Source : Magn Reson Med. 92(4):1348-1362 URL : https://stacks.cdc.gov/view/cdc/159345 Davies-Jenkins, Christopher W. et al. (2024). GABA-edited MEGA-PRESS at 3T: Does a measured MM background improve linear combination modeling?. 92(4). Davies-Jenkins, Christopher W. et al. "GABA-edited MEGA-PRESS at 3T: Does a measured MM background improve linear combination modeling?" 92, no. 4 (2024) Davies-Jenkins, Christopher W. et al. "GABA-edited MEGA-PRESS at 3T: Does a measured MM background improve linear combination modeling?" vol. 92, no. 4, 2024 Export RIS Citation Information.

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Davies-Jenkins, Christopher W. ;

Zöllner, Helge J.

10 2024 | Magn Reson Med. 92(4):1348-1362

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PurposeThe J-difference edited GABA signal is contaminated by other co-edited signals—the largest of which originates from co-edited macromolecules ...

Public Access Available on:

October 01, 2025
Metamodeling for Policy Simulations with Multivariate Outcomes

CITE Title : Metamodeling for Policy Simulations with Multivariate Outcomes Personal Author(s) : Zhong, Huaiyang;Brandeau, Margaret L.;Yazdi, Golnaz Eftekhari;Wang, Jianing;Nolen, Shayla;Hagan, Liesl;Thompson, William W.;Assoumou, Sabrina A.;Linas, Benjamin P.;Salomon, Joshua A.; Published Date : 10 2022;10-2022; Source : Med Decis Making. 42(7):872-884 URL : https://stacks.cdc.gov/view/cdc/121079 Zhong, Huaiyang et al. (2022). Metamodeling for Policy Simulations with Multivariate Outcomes. 42(7). Zhong, Huaiyang et al. "Metamodeling for Policy Simulations with Multivariate Outcomes" 42, no. 7 (2022) Zhong, Huaiyang et al. "Metamodeling for Policy Simulations with Multivariate Outcomes" vol. 42, no. 7, 2022 Export RIS Citation Information.

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Zhong, Huaiyang ;

Brandeau, Margaret L.

10 2022 | Med Decis Making. 42(7):872-884

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Purpose:Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using me...