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Estimating statistical characteristi...

Jalal-Kamali, Ali.

Estimating statistical characteristics under interval uncertainty and constraints: Mean, variance, covariance, and correlation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Estimating statistical characteristics under interval uncertainty and constraints: Mean, variance, covariance, and correlation.
作者:	Jalal-Kamali, Ali.
面頁冊數:	59 p.
附註:	Source: Masters Abstracts International, Volume: 51-01.
附註:	Adviser: Vladik Kreinovich.
Contained By:	Masters Abstracts International51-01(E).
標題:	Applied Mathematics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1513642
ISBN:	9781267435897

Estimating statistical characteristics under interval uncertainty and constraints: Mean, variance, covariance, and correlation.
Jalal-Kamali, Ali.

Estimating statistical characteristics under interval uncertainty and constraints: Mean, variance, covariance, and correlation. - 59 p.

Source: Masters Abstracts International, Volume: 51-01.

Thesis (M.S.)--The University of Texas at El Paso, 2011.

In many practical situations, we have a sample of objects of a given type. When we measure the values of a certain quantity x for these objects, we get a sequence of values x1, ..., xn. When the sample is large enough, then the arithmetic mean E of the values xi is a good approximation for the average value of this quantity for all the objects from this class. Other expressions provide a good approximation to statistical characteristics such as variance, covariance, and correlation.

ISBN: 9781267435897Subjects--Topical Terms:

530992
Applied Mathematics.

Estimating statistical characteristics under interval uncertainty and constraints: Mean, variance, covariance, and correlation.
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245 1 0 $a Estimating statistical characteristics under interval uncertainty and constraints: Mean, variance, covariance, and correlation.
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500 $a Source: Masters Abstracts International, Volume: 51-01.
500 $a Adviser: Vladik Kreinovich.
502 $a Thesis (M.S.)--The University of Texas at El Paso, 2011.
520 $a In many practical situations, we have a sample of objects of a given type. When we measure the values of a certain quantity x for these objects, we get a sequence of values x1, ..., xn. When the sample is large enough, then the arithmetic mean E of the values xi is a good approximation for the average value of this quantity for all the objects from this class. Other expressions provide a good approximation to statistical characteristics such as variance, covariance, and correlation.
520 $a The values xi come from measurements, and measurement is never absolutely accurate. Often, the only information that we have about the measurement error is the upper bound Delta i on this error. In this case, once we have the measurement result x&tilde;i, the condition |x&tilde; i -- xi| ≤ Delta i implies that the actual (unknown) value xi belongs to the interval xi =def [x&tilde;i -- Delta i, x&tilde;i + Delta i]. Different values xi ∈ xi from the corresponding intervals lead, in general, to different values of sample mean, sample variance, etc. It is therefore desirable to find the range of possible values of these characteristics when xi ∈ xi.
520 $a It is known that evaluating such ranges is, in general, NP-hard. The main objective of this thesis is to design feasible (i.e., polynomial-time) algorithms for practically important situations. Several such algorithms are described and proved to be correct.
590 $a School code: 0459.
650 4 $a Applied Mathematics. $3 530992
650 4 $a Statistics. $3 182057
650 4 $a Computer Science. $3 212513
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710 2 $a The University of Texas at El Paso. $b Computer Science. $3 603368
773 0 $t Masters Abstracts International $g 51-01(E).
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