Selected Publications

*= student advisee, ^= senior/corresponding author.

C. Heffernan et al., “A machine learning based interrupted time series framework for studying causal changes in air pollution due to policy interventions: A case study in COVID-19 lockdowns,” American Journal of Epidemiology, 2024 (accepted).

Bibtex

@article{covidcits,
  author = {Heffernan, Claire and Koehler, Kirsten and Levy-Zamora, Misti and Buehler, Colby and Gentner, Drew R. and Peng, Roger and Datta, Abhirup},
  title = {{A machine learning based interrupted time series framework for studying causal changes in air pollution due to policy interventions: A case study in COVID-19 lockdowns}},
  journal = {American Journal of Epidemiology},
  year = {2024 (accepted)}
}

J. Fiksel et al., “Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates,” The American Journal of Tropical Medicine and Hygiene, vol. 108, no. 5_Suppl, pp. 66–77, 2023.

Bibtex

@article{vabias,
  author = {Fiksel, Jacob and Gilbert, Brian and Wilson, Emily and Kalter, Henry and Kante, Almamy and Akum, Aveika and Blau, Dianna and Bassat, Quique and Macicame, Ivalda and Gudo, Eduardo Samo and Black, Robert and Zeger, Scott and Amouzou, Agbessi and Datta, Abhirup},
  title = {Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates},
  journal = {The American Journal of Tropical Medicine and Hygiene},
  year = {2023},
  publisher = {The American Society of Tropical Medicine and Hygiene},
  address = {Arlington VA, USA},
  volume = {108},
  number = {5_Suppl},
  doi = {10.4269/ajtmh.22-0318},
  pages = {66 - 77},
  url = {https://www.ajtmh.org/view/journals/tpmd/108/5_Suppl/article-p66.xml}
}

DOI

C. Heffernan, R. Peng, D. R. Gentner, K. Koehler, and A. Datta, “A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data,” The Annals of Applied Statistics, vol. 17, no. 4, pp. 3056–3087, 2023.

Bibtex

@article{kalman,
  author = {Heffernan, Claire and Peng, Roger and Gentner, Drew R. and Koehler, Kirsten and Datta, Abhirup},
  title = {{A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data}},
  volume = {17},
  journal = {The Annals of Applied Statistics},
  number = {4},
  publisher = {Institute of Mathematical Statistics},
  pages = {3056 -- 3087},
  keywords = {Air pollution, Bayesian, Gaussian process, low-cost sensors, spatial statistics},
  year = {2023},
  doi = {10.1214/23-AOAS1751},
  url = {https://doi.org/10.1214/23-AOAS1751}
}

DOI

A. Saha, S. Basu, and A. Datta, “Random Forests for Spatially Dependent Data,” Journal of the American Statistical Association, vol. 118, no. 541, pp. 665–683, 2023.

Bibtex

@article{rfgls,
  author = {Saha, Arkajyoti and Basu, Sumanta and Datta, Abhirup},
  title = {Random Forests for Spatially Dependent Data},
  journal = {Journal of the American Statistical Association},
  volume = {118},
  number = {541},
  pages = {665-683},
  year = {2023},
  publisher = {Taylor & Francis},
  doi = {10.1080/01621459.2021.1950003},
  url = { 
  	
  	https://doi.org/10.1080/01621459.2021.1950003
  	
  	
  	
  	},
  eprint = { 
  	
  	https://doi.org/10.1080/01621459.2021.1950003
  	
  	
  	
  	}
}

DOI

G. Wang, A. Datta, and M. A. Lindquist, “Bayesian Functional Registration of fMRI Activation Maps,” Annals of Applied Statistics, vol. 16, no. 3, pp. 1676–1699, Sep. 2022.

Bibtex

@article{fmri,
  title = {Bayesian Functional Registration of fMRI Activation Maps},
  author = {Wang, Guoqing and Datta, Abhirup and Lindquist, {Martin A.}},
  year = {2022},
  month = sep,
  doi = {10.1214/21-AOAS1562},
  language = {English (US)},
  volume = {16},
  pages = {1676--1699},
  journal = {Annals of Applied Statistics},
  issn = {1932-6157},
  publisher = {Institute of Mathematical Statistics},
  number = {3}
}

DOI

A. O. Finley, A. Datta, and S. Banerjee, “spNNGP R Package for Nearest Neighbor Gaussian Process Models,” Journal of Statistical Software, vol. 103, no. 5, pp. 1–40, 2022.
Bibtex
```
@article{spnngppaper,
  title = {spNNGP R Package for Nearest Neighbor Gaussian Process Models},
  volume = {103},
  url = {https://www.jstatsoft.org/index.php/jss/article/view/v103i05},
  doi = {10.18637/jss.v103.i05},
  number = {5},
  journal = {Journal of Statistical Software},
  author = {Finley, Andrew O. and Datta, Abhirup and Banerjee, Sudipto},
  year = {2022},
  pages = {1–40}
}
```
Description

<p>This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian pointreferenced outcomes that are spatially indexed. The package implements several Markov chain Monte Carlo (MCMC) and MCMC-free nearest neighbor Gaussian process (NNGP) models for inference about large spatial data. Non-Gaussian outcomes are modeled using a NNGP Pólya-Gamma latent variable. OpenMP parallelization options are provided to take advantage of multiprocessor systems. Package features are illustrated using simulated and real data sets.</p>

DOI

J. Fiksel, S. Zeger, and A. Datta, “A transformation-free linear regression for compositional outcomes and predictors,” Biometrics, vol. 78, no. 3, pp. 974–987, 2022.

Bibtex

@article{compreg,
  author = {Fiksel, Jacob and Zeger, Scott and Datta, Abhirup},
  title = {A transformation-free linear regression for compositional outcomes and predictors},
  journal = {Biometrics},
  volume = {78},
  number = {3},
  pages = {974-987},
  keywords = {compositional data, expectation-maximization algorithm, estimating equation, Kullback–Leibler distance loss function, transformation-free},
  doi = {https://doi.org/10.1111/biom.13465},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13465},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13465},
  year = {2022}
}

DOI

J. Fiksel, A. Datta, A. Amouzou, and S. Zeger, “Generalized Bayes Quantification Learning under Dataset Shift,” Journal of the American Statistical Association, vol. 117, no. 540, pp. 2163–2181, 2022.

Bibtex

@article{gbql,
  author = {Fiksel, Jacob and Datta, Abhirup and Amouzou, Agbessi and Zeger, Scott},
  title = {Generalized Bayes Quantification Learning under Dataset Shift},
  journal = {Journal of the American Statistical Association},
  volume = {117},
  number = {540},
  pages = {2163-2181},
  year = {2022},
  publisher = {Taylor & Francis},
  doi = {10.1080/01621459.2021.1909599},
  url = { 
  	
  	https://doi.org/10.1080/01621459.2021.1909599
  	
  	
  	
  	},
  eprint = { 
  	https://doi.org/10.1080/01621459.2021.1909599
  	}
}

DOI

D. Dey, A. Datta, and S. Banerjee, “Graphical Gaussian process models for highly multivariate spatial data,” Biometrika, vol. 109, no. 4, pp. 993–1014, Dec. 2021.

Bibtex

@article{ggp,
  author = {Dey, Debangan and Datta, Abhirup and Banerjee, Sudipto},
  title = {{Graphical Gaussian process models for highly multivariate spatial data}},
  journal = {Biometrika},
  volume = {109},
  number = {4},
  pages = {993-1014},
  year = {2021},
  month = dec,
  status = {},
  issn = {1464-3510},
  doi = {10.1093/biomet/asab061},
  url = {https://doi.org/10.1093/biomet/asab061},
  eprint = {https://academic.oup.com/biomet/article-pdf/109/4/993/47415795/asab061.pdf}
}

DOI

^A. Datta et al., “Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore,” Atmospheric Environment, vol. 242, 117761, pp. ISSN 1352–2310, 2020.

Bibtex

@article{baltpm,
  title = {Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore},
  author = {Datta, ^Abhirup and Saha, *Arkajyoti and Levy Zamora, Misti and Buehler, Colby and Hao, Lei and Xiong, Fulizi and Gentner, Drew R. and Koehler, Kirsten},
  year = {2020},
  journal = {Atmospheric Environment},
  pages = {ISSN 1352-2310},
  volume = {242, 117761},
  doi = {/10.1016/j.atmosenv.2020.117761}
}

DOI

^A. Datta, *A. Pita, A. Rao, B. Sithole, Z. Mnisi, and S. Baral, “Size Estimation of Key Populations in the HIV Epidemic in eSwatini using incomplete and misaligned capture-recapture data,” Annals of Applied Statistics, vol. 14(3), pp. 1207–1241, 2020.

Bibtex

@article{esw,
  title = {Size Estimation of Key Populations in the HIV Epidemic in eSwatini using incomplete and misaligned capture-recapture data},
  author = {Datta, ^Abhirup and Pita, *Andrew and Rao, Amrita and Sithole, Bhekie and Mnisi, Zandile and Baral, Stefan},
  year = {2020},
  publisher = {},
  status = {},
  journal = {Annals of Applied Statistics},
  volume = {14(3)},
  pages = {1207–1241},
  doi = {10.1214/20-AOAS1327}
}

DOI

^A. Datta, *J. Fiksel, A. Amouzou, and S. Zeger, “Regularized Bayesian transfer learning for population level etiological distributions,” Biostatistics, pp. ISSN 1465–4644, 2020.

Bibtex

@article{caliva,
  title = {Regularized Bayesian transfer learning for population level etiological
  	distributions},
  author = {Datta, ^A. and Fiksel, *J. and Amouzou, A. and Zeger, S.},
  year = {2020},
  journal = {Biostatistics},
  pages = {ISSN 1465–4644},
  publisher = {},
  doi = {/10.1093/biostatistics/kxaa001},
  url = {https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxaa001/5732874}
}

DOI

^A. Datta and H. Zou, “A note on cross-validation for Lasso under measurement errors,” Technometrics, pp. 1–8, 2019.

Bibtex

@article{cvinc,
  title = {A note on cross-validation for Lasso under measurement errors},
  author = {Datta, ^A. and Zou, H.},
  year = {2019},
  publisher = {},
  status = {},
  pages = {1--8},
  journal = {Technometrics},
  url = {https://www.tandfonline.com/doi/full/10.1080/00401706.2019.1668856},
  doi = {10.1080/00401706.2019.1668856}
}

DOI

A. Datta, S. Banerjee, and J. S. Hodges, “Spatial disease mapping using Directed Acyclic Graph Auto-Regressive (DAGAR) models,” Bayesian Analysis, vol. 14, no. 4, pp. 1221–1244, 2019.

Bibtex

@article{dagar,
  title = {Spatial disease mapping using Directed Acyclic Graph Auto-Regressive (DAGAR) models},
  author = {Datta, A. and Banerjee, S. and Hodges, J. S.},
  year = {2019},
  publisher = {},
  pages = {1221--1244},
  status = {},
  volume = {14},
  number = {4},
  url = {https://projecteuclid.org/euclid.ba/1570068455},
  doi = {doi:10.1214/19-BA1177},
  journal = {Bayesian Analysis}
}

DOI

D. Taylor-Rodriguez et al., “Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping,” Statistica Sinica, vol. 29, pp. 1155–1180, 2019.

Bibtex

@article{factornngp,
  title = {Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping},
  author = {Taylor-Rodriguez, Daniel and Finley, Andrew O. and Datta, Abhirup and Babcock, Chad and Andersen, Hans-Erik and Cook, Bruce D. and Morton, Douglas C. and Banerjee, Sudipto},
  year = {2019},
  journal = {Statistica Sinica},
  publisher = {},
  pages = {1155--1180},
  status = {},
  volume = {29},
  url = {http://www3.stat.sinica.edu.tw/statistica//J29N3/j29n34/j29n34.html},
  doi = {10.5705/ss.202018.0005}
}

DOI

A. O. Finley, A. Datta, B. D. Cook, D. C. Morton, H. E. Andersen, and S. Banerjee, “Efficient algorithms for Bayesian nearest neighbor Gaussian processes,” Journal of Computational and Graphical Statistics, pp. 1–14, 2019.

Bibtex

@article{finley2019efficient,
  title = {Efficient algorithms for Bayesian nearest neighbor Gaussian processes},
  author = {Finley, Andrew O and Datta, Abhirup and Cook, Bruce D and Morton, Douglas C and Andersen, Hans E and Banerjee, Sudipto},
  journal = {Journal of Computational and Graphical Statistics},
  pages = {1--14},
  year = {2019},
  publisher = {Taylor \& Francis},
  doi = {10.1080/10618600.2018.1537924}
}

DOI

M. J. Heaton et al., “A case study competition among methods for analyzing large spatial data,” Journal of Agricultural, Biological and Environmental Statistics, vol. 24, no. 3, pp. 398–425, 2019.

Bibtex

@article{heaton2019case,
  title = {A case study competition among methods for analyzing large spatial data},
  author = {Heaton, Matthew J and Datta, Abhirup and Finley, Andrew O and Furrer, Reinhard and Guinness, Joseph and Guhaniyogi, Rajarshi and Gerber, Florian and Gramacy, Robert B and Hammerling, Dorit and Katzfuss, Matthias and others},
  journal = {Journal of Agricultural, Biological and Environmental Statistics},
  volume = {24},
  number = {3},
  pages = {398--425},
  year = {2019},
  publisher = {Springer},
  doi = {10.1007/s13253-018-00348-w}
}

DOI

E. E. Butler and A. Datta (co-first authors) et al., “Mapping local and global variability in plant trait distributions,” Proceedings of the National Academy of Sciences, vol. 114, no. 51, pp. E10937–E10946, 2017.

Bibtex

@article{traitmap,
  author = {{E. E. Butler and A. Datta (co-first authors)} and Flores-Moreno, Habacuc and Chen, Ming and Wythers, Kirk R. and Fazayeli, Farideh and Banerjee, Arindam and Atkin, Owen K. and Kattge, Jens and Amiaud, Bernard and Blonder, Benjamin and Boenisch, Gerhard and Bond-Lamberty, Ben and Brown, Kerry A. and Byun, Chaeho and Campetella, Giandiego and Cerabolini, Bruno E. L. and Cornelissen, Johannes H. C. and Craine, Joseph M. and Craven, Dylan and de Vries, Franciska T. and D{\'\i}az, Sandra and Domingues, Tomas F. and Forey, Estelle and Gonz{\'a}lez-Melo, Andr{\'e}s and Gross, Nicolas and Han, Wenxuan and Hattingh, Wesley N. and Hickler, Thomas and Jansen, Steven and Kramer, Koen and Kraft, Nathan J. B. and Kurokawa, Hiroko and Laughlin, Daniel C. and Meir, Patrick and Minden, Vanessa and Niinemets, {\"U}lo and Onoda, Yusuke and Pe{\~n}uelas, Josep and Read, Quentin and Sack, Lawren and Schamp, Brandon and Soudzilovskaia, Nadejda A. and Spasojevic, Marko J. and Sosinski, Enio and Thornton, Peter E. and Valladares, Fernando and van Bodegom, Peter M. and Williams, Mathew and Wirth, Christian and Reich, Peter B.},
  title = {Mapping local and global variability in plant trait distributions},
  volume = {114},
  number = {51},
  pages = {E10937--E10946},
  year = {2017},
  doi = {10.1073/pnas.1708984114},
  publisher = {National Academy of Sciences},
  issn = {0027-8424},
  url = {http://www.pnas.org/content/114/51/E10937},
  eprint = {http://www.pnas.org/content/114/51/E10937.full.pdf},
  journal = {Proceedings of the National Academy of Sciences}
}

DOI

A. Datta and H. Zou, “CoCoLasso for high-dimensional error-in-variables regression,” Annals of Statistics, vol. 45, no. 6, pp. 2400–2426, 2017.

Bibtex

@article{coco,
  author = {Datta, Abhirup and Zou, Hui},
  doi = {10.1214/16-AOS1527},
  fjournal = {The Annals of Statistics},
  journal = {Annals of Statistics},
  number = {6},
  pages = {2400--2426},
  publisher = {The Institute of Mathematical Statistics},
  title = {CoCoLasso for high-dimensional error-in-variables regression},
  url = {https://doi.org/10.1214/16-AOS1527},
  volume = {45},
  year = {2017}
}

DOI

A. Datta, S. Banerjee, A. O. Finley, N. A. S. Hamm, and M. Schaap, “Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis,” Annals of Applied Statistics, vol. 10, no. 3, pp. 1286–1316, 2016.

Bibtex

@article{dnngp,
  author = {Datta, Abhirup and Banerjee, Sudipto and Finley, Andrew O. and Hamm, Nicholas A. S. and Schaap, Martijn},
  title = {Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis},
  journal = {Annals of Applied Statistics},
  fjournal = {Annals of Applied Statistics},
  year = {2016},
  volume = {10},
  number = {3},
  pages = {1286-1316},
  issn = {1932-6157},
  doi = {10.1214/16-AOAS931},
  sici = {1932-6157(2016)10:3<1286:NDNNGP>2.0.CO;2-Y}
}

DOI

A. Datta, S. Banerjee, A. O. Finley, and A. E. Gelfand, “Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets,” Journal of the American Statistical Association Theory and Methods, vol. 111, no. 514, pp. 800–812, 2016.

Bibtex

@article{nngp,
  author = {Datta, Abhirup and Banerjee, Sudipto and Finley, Andrew O. and Gelfand, Alan E.},
  title = {{Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets}},
  journal = {Journal of the American Statistical Association Theory and Methods},
  volume = {111},
  number = {514},
  pages = {800--812},
  year = {2016},
  doi = {10.1080/01621459.2015.1044091},
  url = { http://dx.doi.org/10.1080/01621459.2015.1044091 },
  eprint = { http://dx.doi.org/10.1080/01621459.2015.1044091 }
}

DOI

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Selected Publications