Research output: Contribution to journal › Article › peer-review
Maximum compatibility method for data fitting under interval uncertainty. / Shary, S. P.
In: Journal of Computer and Systems Sciences International, Vol. 56, No. 6, 01.11.2017, p. 897-913.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Maximum compatibility method for data fitting under interval uncertainty
AU - Shary, S. P.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - For the linear regression model, the data-fitting problem under the interval uncertainty of the data is studied. As an estimate of the linear function parameters, it is proposed to take their values that deliver the maximum for the so-called recognizing functional of the parameter set compatible with the data (the maximum compatibility method). The properties of the recognizing functional, its interpretation, and the properties of the estimates obtained using the maximum compatibility method are investigated. The relationships to other data analysis methods are discussed, and a practical electrochemistry problem is solved.
AB - For the linear regression model, the data-fitting problem under the interval uncertainty of the data is studied. As an estimate of the linear function parameters, it is proposed to take their values that deliver the maximum for the so-called recognizing functional of the parameter set compatible with the data (the maximum compatibility method). The properties of the recognizing functional, its interpretation, and the properties of the estimates obtained using the maximum compatibility method are investigated. The relationships to other data analysis methods are discussed, and a practical electrochemistry problem is solved.
UR - http://www.scopus.com/inward/record.url?scp=85040811132&partnerID=8YFLogxK
U2 - 10.1134/S1064230717050100
DO - 10.1134/S1064230717050100
M3 - Article
AN - SCOPUS:85040811132
VL - 56
SP - 897
EP - 913
JO - Journal of Computer and Systems Sciences International
JF - Journal of Computer and Systems Sciences International
SN - 1064-2307
IS - 6
ER -
ID: 9179192