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Probability approximations with appl...

Wu, Yichao.

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Probability approximations with applications in computational finance and computational biology.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probability approximations with applications in computational finance and computational biology./
作者:	Wu, Yichao.
面頁冊數:	79 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0967.
Contained By:	Dissertation Abstracts International67-02B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3207426
ISBN:	9780542547614

Probability approximations with applications in computational finance and computational biology.
Wu, Yichao.

Probability approximations with applications in computational finance and computational biology. - 79 p.

Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0967.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2006.

In this work, certain probability approximation schemes are applied to two different contexts: one under stochastic volatility models in financial econometrics and the other about the hierarchical clustering of directional data on the unit (hyper)sphere. In both cases, approximations play an important role in improving the computational efficiency.

ISBN: 9780542547614Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Probability approximations with applications in computational finance and computational biology.
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245 1 0 $a Probability approximations with applications in computational finance and computational biology.
300 $a 79 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0967.
500 $a Advisers: Chuanshu Ji; Harry Hurd.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2006.
520 $a In this work, certain probability approximation schemes are applied to two different contexts: one under stochastic volatility models in financial econometrics and the other about the hierarchical clustering of directional data on the unit (hyper)sphere. In both cases, approximations play an important role in improving the computational efficiency.
520 $a In the first part, we study stochastic volatility models. As an indispensable part of Bayesian inference using MCMC, we need to compute the option prices for each iteration at each time. To facilitate the computation, an approximation scheme is proposed for numerical computation of the option prices based on a central limit theorem, and some error bounds for the approximations are obtained.
520 $a The second part of the work originates from studying microarray data. After pre-processing the microarray data, each gene is represented by a unit vector. To study their patterns, we adopt hierarchical clustering and introduce the idea of linking by the size of a spherical cap. In this way, each cluster is represented by a spherical cap. By studying the distribution of direction data on the unit (hyper)sphere, we can assess the significance of observing a big cluster using Poisson approximations.
590 $a School code: 0153.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
650 4 $a Economics, Finance. $3 626650
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710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
773 0 $t Dissertation Abstracts International $g 67-02B.
790 1 0 $a Ji, Chuanshu, $e advisor
790 1 0 $a Hurd, Harry, $e advisor
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