Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Undifferentiated optimization of data sample. / Orekhov, Andrey V.
2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings. ed. / L. N. Polyakova. Institute of Electrical and Electronics Engineers Inc., 2017. p. 229-231 7973995.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Undifferentiated optimization of data sample
AU - Orekhov, Andrey V.
PY - 2017/7/10
Y1 - 2017/7/10
N2 - Obtaining an unbiased data sample is an important task in the statistical analysis of experimental data. The unbiased data sample is a representative data sample. The natural desire is to obtain a representative data sample using computational methods. A procedure for adjusting the structure of the data sample in line with the structure of statistical population is called 'correction of a data sample'. This procedure optimizes data sample, minimizing the difference between a theoretical distribution of control variables and an empirical distribution of control variables. The variables are called control ones if we know the distribution of their spectral values in the statistical population. All of the known methods of adjusting the data sample have significant drawback, as they 'correct' an empirical distribution function, but not the data sample. For example, that refers to IPF algorithm [1], [2]. We discuss an algorithm that corrects sample data rather than their empirical distributions. This algorithm is randomized. An algorithm is called randomized, if the execution of one or several iterations relies on a random rule [3]. The optimization of a data sample carried out with a randomized algorithm cannot be differentiable. This algorithm can be considered as the inhomogeneous Markov chain [4].
AB - Obtaining an unbiased data sample is an important task in the statistical analysis of experimental data. The unbiased data sample is a representative data sample. The natural desire is to obtain a representative data sample using computational methods. A procedure for adjusting the structure of the data sample in line with the structure of statistical population is called 'correction of a data sample'. This procedure optimizes data sample, minimizing the difference between a theoretical distribution of control variables and an empirical distribution of control variables. The variables are called control ones if we know the distribution of their spectral values in the statistical population. All of the known methods of adjusting the data sample have significant drawback, as they 'correct' an empirical distribution function, but not the data sample. For example, that refers to IPF algorithm [1], [2]. We discuss an algorithm that corrects sample data rather than their empirical distributions. This algorithm is randomized. An algorithm is called randomized, if the execution of one or several iterations relies on a random rule [3]. The optimization of a data sample carried out with a randomized algorithm cannot be differentiable. This algorithm can be considered as the inhomogeneous Markov chain [4].
KW - EXPECTED MARGINAL TOTALS
KW - TABLES
UR - http://www.scopus.com/inward/record.url?scp=85027454747&partnerID=8YFLogxK
U2 - 10.1109/CNSA.2017.7973995
DO - 10.1109/CNSA.2017.7973995
M3 - Conference contribution
AN - SCOPUS:85027454747
SP - 229
EP - 231
BT - 2017 Constructive Nonsmooth Analysis and Related Topics (Dedicated to the Memory of V.F. Demyanov), CNSA 2017 - Proceedings
A2 - Polyakova, L. N.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Constructive Nonsmooth Analysis and Related Topics
Y2 - 22 May 2017 through 27 May 2017
ER -
ID: 36156064