55 citations · 235 across the 17 of their papers we have counts for
11 papers · 1 filter
Bootstrap inference in functional linear regression models with scalar response under heteroscedasticity
Hyemin Yeon, Xiongtao Dai, Daniel John Nordman
Inference for functional linear models in the presence of heteroscedastic errors has received insufficient attention given its practical importance; in fact, even a central limit t…
On optimal block resampling for Gaussian-subordinated long-range dependent processes
Qihao Zhang, Soumendra N. Lahiri, Daniel J. Nordman
Block-based resampling estimators have been intensively investigated for weakly dependent time processes, which has helped to inform implementation (e.g., best block sizes). Howeve…
Convolved subsampling estimation with applications to block bootstrap
Johannes Tewes, Daniel J. Nordman, Dimitris N. Politis
The block bootstrap approximates sampling distributions from dependent data by resampling data blocks. A fundamental problem is establishing its consistency for the distribution of…
On the instability and degeneracy of deep learning models
Andee Kaplan, Daniel Nordman, Stephen Vardeman
A probability model exhibits instability if small changes in a data outcome result in large, and often unanticipated, changes in probability. This instability is a property of the…
A frequency domain empirical likelihood method for irregularly spaced spatial data
Soutir Bandyopadhyay, Soumendra N. Lahiri, Daniel J. Nordman
This paper develops empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. Unlike the frequency domain empirical likelihood (FDEL) methodolog…
Convergence rates of empirical block length selectors for block bootstrap
Daniel J. Nordman, Soumendra N. Lahiri
We investigate the accuracy of two general non-parametric methods for estimating optimal block lengths for block bootstraps with time series - the first proposed in the seminal pap…
A nonstandard empirical likelihood for time series
Daniel J. Nordman, Helle Bunzel, Soumendra N. Lahiri
Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence reg…
Goodness of fit tests for a class of Markov random field models
Mark S. Kaiser, Soumendra N. Lahiri, Daniel J. Nordman
This paper develops goodness of fit statistics that can be used to formally assess Markov random field models for spatial data, when the model distributions are discrete or continu…
A note on the stationary bootstrap's variance
Daniel J. Nordman
Because the stationary bootstrap resamples data blocks of random length, this method has been thought to have the largest asymptotic variance among block bootstraps Lahiri [Ann. St…
A frequency domain empirical likelihood for short- and long-range dependence
Daniel J. Nordman, Soumendra N. Lahiri
This paper introduces a version of empirical likelihood based on the periodogram and spectral estimating equations. This formulation handles dependent data through a data transform…
On optimal spatial subsample size for variance estimation
Daniel J. Nordman, Soumendra N. Lahiri
We consider the problem of determining the optimal block (or subsample) size for a spatial subsampling method for spatial processes observed on regular grids. We derive expansions…