Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Nordman, Daniel J."'
Nonparametric cointegrating regression models have been extensively used in financial markets, stock prices, heavy traffic, climate data sets, and energy markets. Models with parametric regression functions can be more appealing in practice compared
Externí odkaz:
http://arxiv.org/abs/2312.16162
Block-based resampling estimators have been intensively investigated for weakly dependent time processes, which has helped to inform implementation (e.g., best block sizes). However, little is known about resampling performance and block sizes under
Externí odkaz:
http://arxiv.org/abs/2208.01713
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 (depending on the remaining lifetime of a fleet of systems), the needed am
Externí odkaz:
http://arxiv.org/abs/2109.13970
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 include the plug-in, pivotal, and calibration methods. Then we describe met
Externí odkaz:
http://arxiv.org/abs/2011.03065
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 prediction of warranty returns and the prediction of the number of future produ
Externí odkaz:
http://arxiv.org/abs/2007.08648
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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 simulation from such MRF models is often an important consideration, par
Externí odkaz:
http://arxiv.org/abs/1808.04739
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 sample mean, as a prototypical statistic. We use a structural relat
Externí odkaz:
http://arxiv.org/abs/1706.07237
Publikováno v:
Annals of Statistics 2015, Vol. 43, 519-545
This paper develops empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. Unlike the frequency domain empirical likelihood (FDEL) methodology for time series (on a regular grid), the formulation of the spatial
Externí odkaz:
http://arxiv.org/abs/1503.04985
Publikováno v:
Journal of Computational & Graphical Statistics; Jul-Sep2024, Vol. 33 Issue 3, p774-786, 13p