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Extreme value theory: Applications ...

Laflamme, Eric Matthew.

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Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes./
作者:	Laflamme, Eric Matthew.
面頁冊數:	159 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3575984
ISBN:	9781303551406

Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes.
Laflamme, Eric Matthew.

Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes. - 159 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--University of New Hampshire, 2013.

This work explores two applications of extreme value analysis. First, we apply EV techniques to traffic stream data to develop an accurate distribution of capacity. Data were collected by the NHDOT along Interstate I93, and two adjacent locations in Salem, NH were examined. Daily flow maxima were used to estimate capacity, and data not associated with daily breakdown were deemed censored values. Under this definition, capacity values are approximated by the generalized extreme value (GEV) distribution for block maxima. To address small sample sizes and the presence of censoring, a Bayesian framework using semi-informative priors was implemented. A simple cross validation procedure reveals the GEV model, using both censored and observed capacity data, is suitable for probabilistic prediction. To overcome the uncertainty associated with a high number of censored values at one location, a hierarchical model was developed to share information between locations and generally improve fitted results.

ISBN: 9781303551406Subjects--Topical Terms:

517247
Statistics.

Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes.
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500 $a Adviser: Ernst Linder.
502 $a Thesis (Ph.D.)--University of New Hampshire, 2013.
520 $a This work explores two applications of extreme value analysis. First, we apply EV techniques to traffic stream data to develop an accurate distribution of capacity. Data were collected by the NHDOT along Interstate I93, and two adjacent locations in Salem, NH were examined. Daily flow maxima were used to estimate capacity, and data not associated with daily breakdown were deemed censored values. Under this definition, capacity values are approximated by the generalized extreme value (GEV) distribution for block maxima. To address small sample sizes and the presence of censoring, a Bayesian framework using semi-informative priors was implemented. A simple cross validation procedure reveals the GEV model, using both censored and observed capacity data, is suitable for probabilistic prediction. To overcome the uncertainty associated with a high number of censored values at one location, a hierarchical model was developed to share information between locations and generally improve fitted results.
520 $a Next, we perform a statistical downscaling by applying a CDF transformation function to local-level daily precipitation extremes (from NCDC station data) and corresponding NARCCAP regional climate model (RCM) output to derive local-scale projections. These high-resolution projections are essential in assessing the impacts of projected climate change. The downscaling method is performed on 58 locations throughout New England, and from the projected distribution of extreme precipitation local-level 25-year return levels are calculated. To obtain uncertainty estimates for future return levels, both a parametric bootstrap and Bayesian procedure are implemented. The Bayesian method consists of a semi-parametric mixture model for daily precipitation where extremes are modeled parametrically using generalized Pareto distributions, and non-extremes are modeled non-parametrically using quantiles. We find that these Bayesian credibility intervals are generally larger than those obtained from a previously applied parametric Bootstrap procedure, indicating that projected trends in New England precipitation tend to be less significant than is hinted at in many studies.
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