43 citations · 84 across the 4 of their papers we have counts for
8 papers
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…
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…