Dan Kluger

I am a Michael Hammer Postdoctoral Fellow at the MIT Institute for Data, Systems, and Society, where I am fortunate to be hosted by Professors Sherrie Wang and Stephen Bates. As a statistician and interdisciplinary researcher, I am broadly interested in developing and deploying statistical methods for applications in agriculture and remote sensing. My current research is on methods for conducting reliable statistical analyses that leverage widely available, yet error-prone proxies.

Recently, I completed my PhD in Statistics at Stanford University, where I was grateful to be advised by Professors Art Owen and David Lobell and supported as a James and Nancy Kelso Interdisciplinary Graduate Fellow. While at Stanford, my research areas included multiple hypothesis testing, causal inference, data fusion, measurement error, crop rotation, and crop type mapping. Prior to graduate school, I received a B.S. in Mathematics and a B.A. in Statistics & Data Science at Yale University.

Selected Papers

Biases in estimates of air pollution impacts: the role of omitted variables and measurement errors

Dan M. Kluger, David B. Lobell, and Art B. Owen

2024

arXiv

A central limit theorem for the Benjamini-Hochberg false discovery proportion under a factor model

Dan M. Kluger, and Art B. Owen

Bernoulli, 2024

arXiv Bib HTML Video

@article{KO24_BHCLTunderDependence,
  author = {Kluger, Dan M. and Owen, Art B.},
  title = {{A central limit theorem for the Benjamini-Hochberg false discovery proportion under a factor model}},
  volume = {30},
  journal = {Bernoulli},
  number = {1},
  publisher = {Bernoulli Society for Mathematical Statistics and Probability},
  pages = {743 -- 769},
  keywords = {Empirical cumulative distribution function, functional central limit theorem, Functional Delta Method, multiple hypothesis testing, Simes line},
  year = {2024},
  doi = {10.3150/23-BEJ1615},
  url = {https://doi.org/10.3150/23-BEJ1615},
}

Kernel regression analysis of tie-breaker designs

Dan M. Kluger, and Art B. Owen

Electronic Journal of Statistics, 2023

arXiv Bib HTML

@article{KO23_TieBreakerKernelRegAnalysis,
  author = {Kluger, Dan M. and Owen, Art B.},
  title = {{Kernel regression analysis of tie-breaker designs}},
  volume = {17},
  journal = {Electronic Journal of Statistics},
  number = {1},
  publisher = {Institute of Mathematical Statistics and Bernoulli Society},
  pages = {243 -- 290},
  keywords = {Causal inference, Experimental design, hybrid experiments, local linear regression, regression discontinuity designs},
  year = {2023},
  doi = {10.1214/23-EJS2102},
  url = {https://doi.org/10.1214/23-EJS2102},
}

Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields

Dan M Kluger, Art B Owen, and David B Lobell

Environmental Research Letters, 2022

Bib HTML Blog

@article{KOL22_ExperimentalObservationalCropRotation,
  doi = {10.1088/1748-9326/ac6083},
  url = {https://dx.doi.org/10.1088/1748-9326/ac6083},
  year = {2022},
  publisher = {IOP Publishing},
  volume = {17},
  number = {4},
  pages = {044066},
  author = {Kluger, Dan M and Owen, Art B and Lobell, David B},
  title = {Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields},
  journal = {Environmental Research Letters},
}

Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions

Dan M. Kluger, Sherrie Wang, and David B. Lobell

Remote Sensing of Environment, 2021

arXiv Bib HTML Blog Code

@article{KWL21_CropMappingDistributionalShift,
  title = {Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions},
  journal = {Remote Sensing of Environment},
  volume = {262},
  pages = {112488},
  year = {2021},
  issn = {0034-4257},
  doi = {https://doi.org/10.1016/j.rse.2021.112488},
  url = {https://www.sciencedirect.com/science/article/pii/S0034425721002066},
  author = {Kluger, Dan M. and Wang, Sherrie and Lobell, David B.},
  keywords = {Remote sensing, Crop mapping, Agriculture, Sentinel-2, Machine Learning, Transfer learning, Anticausal Learning, Domain Shift, Classification},
}