NDLI: On the detection of LRD phenomena

Content Provider	IEEE Xplore Digital Library
Author	Kettani, H. Gubner, J.A.
Copyright Year	2012
Description	Author affiliation: ECECS Department, Polytechnic University of Puerto Rico, San Juan, Puerto Rico 00919 (Kettani, H.) \|\| ECE Department, University of Wisconsin, Madison, Wisconsin 53706 (Gubner, J.A.)
Abstract	A new model-testing paradigm is introduced. The proposed method uses the structure of the autocorrelation function of the model that we would like to fit the data to. We start by estimating the autocorrelation function of the data, and then apply a curve-fitting criterion, which we call the optimization method. If the resultant error is high, then the given process fails the test and may not be considered to follow that particular model. Otherwise, the process is assumed to be long-range dependent following the assumed model with the parameter as estimated. The criteria that we followed to decide whether the error is large or not is the probability of false alarm. For a wide range of the parameter, we develop a relation between the the probability of false alarm and the cutoff to decide how large the error can be. This paradigm is illustrated through the Second-Order Self-Similar (SOSS) model which is an example of a Long-Range Dependent (LRD) model. We perform an empirical study using artificial SOSS data to make a proper decision on the cutoff and to obtain empirical confidence intervals and bias. To better evaluate this proposed method, we tested the method on two sets of real data that are known to be LRD, and on artificial and real data that is known not to be LRD. Although we focus on processes that are LRD, the new method is readily generalizable to other processes. In other words, the new method can be used as a tool to check the validity of a model in characterizing certain process.
Starting Page	320
Ending Page	326
File Size	210323
Page Count	7
File Format	PDF
ISBN	9781467300087
e-ISBN	9781467300094
e-ISBN	9781467307239
DOI	10.1109/ICCNC.2012.6167436
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Limiting Computational modeling Time series analysis Second-Order Self-Similarity (SOSS) Estimation Routing protocols Internet Yttrium Network Traffic Long-Range Dependence (LRD)
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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