A blockwise empirical likelihood method for time series in frequency domain inference von Yu, Haihan, Kaiser, Mark S., Nordman, Daniel J.

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A formal goodness-of-fit test for spatial binary Markov random field models von Biswas, Eva, Kaplan, Andee, Kaiser, Mark S, Nordman, Daniel J

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A subsampling perspective for extending the validity of state-of-the-art bootstraps in the frequency domain von Yu, Haihan, Kaiser, Mark S, Nordman, Daniel J

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Predicting the Number of Future Events von Tian, Qinglong, Meng, Fanqi, Nordman, Daniel J., Meeker, William Q.

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On optimal block resampling for Gaussian-subordinated long-range dependent processes von Zhang, Qihao, Lahiri, Soumendra N., Nordman, Daniel J.

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Methods to Compute Prediction Intervals: A Review and New Results von Tian, Qinglong, Nordman, Daniel J., Meeker, William Q.

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Case-Specific Random Forests von Xu, Ruo, Nettleton, Dan, Nordman, Daniel J.

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CONVOLVED SUBSAMPLING ESTIMATION WITH APPLICATIONS TO BLOCK BOOTSTRAP von Tewes, Johannes, Politis, Dimitris N., Nordman, Daniel J.

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A FREQUENCY DOMAIN EMPIRICAL LIKELIHOOD METHOD FOR IRREGULARLY SPACED SPATIAL DATA von Bandyopadhyay, Soutir, Lahiri, Soumendra N., Nordman, Daniel J.

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A SMOOTH BLOCK BOOTSTRAP FOR QUANTILE REGRESSION WITH TIME SERIES von Gregory, Karl B., Lahiri, Soumendra N., Nordman, Daniel J.

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Local structure graph models with higher‐order dependence von Casleton, Emily M., Nordman, Daniel J., Kaiser, Mark S.

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The Number of MCMC Draws Needed to Compute Bayesian Credible Bounds von Liu, Jia, Nordman, Daniel J., Meeker, William Q.

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A Note on the Stationary Bootstrap's Variance von Nordman, Daniel J.

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Empirical Likelihood for a Long Range Dependent Process Subordinated to a Gaussian Process von Lahiri, Soumendra N., Das, Ujjwal, Nordman, Daniel J.

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Progressive Block Empirical Likelihood Method for Time Series von Kim, Young Min, Lahiri, Soumendra N, Nordman, Daniel J

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A frequency domain bootstrap for Whittle estimation under long-range dependence von Kim, Young Min, Nordman, Daniel J.

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A NONSTANDARD EMPIRICAL LIKELIHOOD FOR TIME SERIES von Nordman, Daniel J., Bunzel, Helle, Lahiri, Soumendra N.

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Non‐Parametric Spectral Density Estimation Under Long‐Range Dependence von Kim, Young Min, Lahiri, Soumendra N., Nordman, Daniel J.

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On the S-instability and degeneracy of discrete deep learning models von Kaplan, Andee, Nordman, Daniel J., Vardeman, Stephen B.

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Bayesian Inference for a New Class of Distributions on Equivalence Classes of Three-Dimensional Orientations With Applications to Materials Science von Du, Chuanlong, Nordman, Daniel J., Vardeman, Stephen B.

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