東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Robust and distributed hypothesis te...

Gul, Gokhan.

Linked to FindBook

Google Book

Amazon

博客來

Robust and distributed hypothesis testing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robust and distributed hypothesis testing/ by Gokhan Gul.
Author:	Gul, Gokhan.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxi, 141 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Background -- Robust Hypothesis Testing with a Single Distance -- Robust Hypothesis Testing with Multiple Distances -- Robust Hypothesis Testing with Repeated Observations -- Robust Decentralized Hypothesis Testing -- Minimax Decentralized Hypothesis Testing -- Conclusions and Outlook.
Contained By:	Springer eBooks
Subject:	Statistical hypothesis testing. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-49286-5
ISBN:	9783319492865

Robust and distributed hypothesis testing
Gul, Gokhan.

Robust and distributed hypothesis testing[electronic resource] /by Gokhan Gul. - Cham :Springer International Publishing :2017. - xxi, 141 p. :ill. (some col.), digital ;24 cm. - Lecture notes in electrical engineering,v.4141876-1100 ;. - Lecture notes in electrical engineering ;v.414..

Introduction -- Background -- Robust Hypothesis Testing with a Single Distance -- Robust Hypothesis Testing with Multiple Distances -- Robust Hypothesis Testing with Repeated Observations -- Robust Decentralized Hypothesis Testing -- Minimax Decentralized Hypothesis Testing -- Conclusions and Outlook.

This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the book describes the derivation of theoretical bounds in minimax decentralized hypothesis testing, which have not yet been known. As a timely report on the state-of-the-art in robust hypothesis testing, this book is mainly intended for postgraduates and researchers in the field of electrical and electronic engineering, statistics and applied probability. Moreover, it may be of interest for students and researchers working in the field of classification, pattern recognition and cognitive radio.

ISBN: 9783319492865

Standard No.: 10.1007/978-3-319-49286-5doiSubjects--Topical Terms:

560235
Statistical hypothesis testing.

LC Class. No.: QA277

Dewey Class. No.: 519.56

Robust and distributed hypothesis testing
LDR:02871nmm a2200349 a 4500 001 2092798
003 DE-He213
005 20170908113345.0
006 m d
007 cr nn 008maaau
008 171117s2017 gw s 0 eng d
020 $a 9783319492865 $q (electronic bk.)
020 $a 9783319492858 $q (paper)
024 7 $a 10.1007/978-3-319-49286-5 $2 doi
035 $a 978-3-319-49286-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA277
072 7 $a TTBM $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a COM073000 $2 bisacsh
082 0 4 $a 519.56 $2 23
090 $a QA277 $b .G971 2017
100 1 $a Gul, Gokhan. $3 3227033
245 1 0 $a Robust and distributed hypothesis testing $h [electronic resource] / $c by Gokhan Gul.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xxi, 141 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in electrical engineering, $x 1876-1100 ; $v v.414
505 0 $a Introduction -- Background -- Robust Hypothesis Testing with a Single Distance -- Robust Hypothesis Testing with Multiple Distances -- Robust Hypothesis Testing with Repeated Observations -- Robust Decentralized Hypothesis Testing -- Minimax Decentralized Hypothesis Testing -- Conclusions and Outlook.
520 $a This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the book describes the derivation of theoretical bounds in minimax decentralized hypothesis testing, which have not yet been known. As a timely report on the state-of-the-art in robust hypothesis testing, this book is mainly intended for postgraduates and researchers in the field of electrical and electronic engineering, statistics and applied probability. Moreover, it may be of interest for students and researchers working in the field of classification, pattern recognition and cognitive radio.
650 0 $a Statistical hypothesis testing. $3 560235
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Pattern Recognition. $3 891045
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Lecture notes in electrical engineering ; $v v.414. $3 3227034
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-49286-5
950 $a Engineering (Springer-11647)