Daniel J. Nordman, Ph.D.
Affiliations: | 2002 | Iowa State University, Ames, IA, United States |
Area:
StatisticsGoogle:
"Daniel Nordman"Parents
Sign in to add mentorSoumendra N. Lahiri | grad student | 2002 | Iowa State | |
(On nonparametric methods for strongly and weakly dependent lattice data.) |
Children
Sign in to add traineeRuo Xu | grad student | 2013 | Iowa State |
Emily T. Casleton | grad student | 2014 | Iowa State |
Matthew M. Van Hala | grad student | 2014 | Iowa State |
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Publications
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Hala MV, Bandyopadhyay S, Lahiri SN, et al. (2020) A general frequency domain method for assessing spatial covariance structures Bernoulli. 26: 2463-2487 |
Kaplan A, Nordman DJ, Vardeman SB. (2020) On the S-instability and degeneracy of discrete deep learning models Information and Inference: a Journal of the Ima. 9: 627-655 |
Tewes J, Nordman DJ, Politis DN. (2019) Convolved subsampling estimation with applications to block bootstrap Annals of Statistics. 47: 468-496 |
Lahiri SN, Das U, Nordman DJ. (2019) Empirical Likelihood for a Long Range Dependent Process Subordinated to a Gaussian Process Journal of Time Series Analysis. 40: 447-466 |
Kaplan A, Kaiser MS, Lahiri SN, et al. (2019) Simulating Markov Random Fields With a Conclique-Based Gibbs Sampler Journal of Computational and Graphical Statistics. 29: 286-296 |
Kaplan A, Nordman DJ, Vardeman SB. (2019) Properties and Bayesian fitting of restricted Boltzmann machines Statistical Analysis and Data Mining. 12: 23-38 |
Gregory KB, Lahiri SN, Nordman DJ. (2018) A smooth block bootstrap for quantile regression with time series The Annals of Statistics. 46: 1138-1166 |
Kim YM, Lahiri SN, Nordman DJ. (2018) Non-Parametric Spectral Density Estimation Under Long-Range Dependence Journal of Time Series Analysis. 39: 380-401 |
Casleton E, Nordman D, Kaiser M. (2017) A local structure model for network analysis Statistics and Its Interface. 10: 355-367 |
Van Hala M, Bandyopadhyay S, Lahiri SN, et al. (2017) On the non-standard distribution of empirical likelihood estimators with spatial data Journal of Statistical Planning and Inference. 187: 109-114 |