55 citations · 235 across the 17 of their papers we have counts for
18 papers
Properties of Test Statistics for Nonparametric Cointegrating Regression Functions Based on Subsamples
Sepideh Mosaferi, Mark S. Kaiser, Daniel J. Nordman
Nonparametric cointegrating regression models have been extensively used in financial markets, stock prices, heavy traffic, climate data sets, and energy markets. Models with param…
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…
Constructing Prediction Intervals Using the Likelihood Ratio Statistic
Qinglong Tian, Daniel J. Nordman, William Q. Meeker
Statistical prediction plays an important role in many decision processes such as university budgeting (depending on the number of students who will enroll), capital budgeting (dep…
Methods to Compute Prediction Intervals: A Review and New Results
Qinglong Tian, Daniel J. Nordman, William Q. Meeker
This paper reviews two main types of prediction interval methods under a parametric framework. First, we describe methods based on an (approximate) pivotal quantity. Examples inclu…
Predicting the Number of Future Events
Qinglong Tian, Fanqi Meng, Daniel J. Nordman +1
This paper describes prediction methods for the number of future events from a population of units associated with an on-going time-to-event process. Examples include the predictio…
Simulating Markov random fields with a conclique-based Gibbs sampler
Andee Kaplan, Mark S. Kaiser, Soumendra N. Lahiri +1
For spatial and network data, we consider models formed from a Markov random field (MRF) structure and the specification of a conditional distribution for each observation. Fast si…
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…
Properties and Bayesian fitting of restricted Boltzmann machines
Andee Kaplan, Daniel Nordman, Stephen Vardeman
A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete or continuous random variables, with two layers, one hidden and one visible, and no c…
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…
Why do simple algorithms for triangle enumeration work in the real world?
Jonathan W. Berry, Luke A. Fostvedt, Daniel J. Nordman +3
Listing all triangles is a fundamental graph operation. Triangles can have important interpretations in real-world graphs, especially social and other interaction networks. Despite…
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…