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Construction of regression experiment optimal plan using parallel computing. / Vladimirova, Ludmila; Fatyanova, Irina.

2015 International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 361-363 7342140.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Vladimirova, L & Fatyanova, I 2015, Construction of regression experiment optimal plan using parallel computing. in 2015 International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015 - Proceedings., 7342140, Institute of Electrical and Electronics Engineers Inc., pp. 361-363, International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015, St. Petersburg, Russian Federation, 5/10/15. https://doi.org/10.1109/SCP.2015.7342140

APA

Vladimirova, L., & Fatyanova, I. (2015). Construction of regression experiment optimal plan using parallel computing. In 2015 International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015 - Proceedings (pp. 361-363). [7342140] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCP.2015.7342140

Vancouver

Vladimirova L, Fatyanova I. Construction of regression experiment optimal plan using parallel computing. In 2015 International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 361-363. 7342140 https://doi.org/10.1109/SCP.2015.7342140

Author

Vladimirova, Ludmila ; Fatyanova, Irina. / Construction of regression experiment optimal plan using parallel computing. 2015 International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 361-363

BibTeX

@inproceedings{29261a9e91ee489dbc303053390e5d9e,
title = "Construction of regression experiment optimal plan using parallel computing",
abstract = "In this paper, we consider the classical linear regression of the second order, the unknown parameters are usually evaluated by the method of least squares. The distribution of the error of parameter vector estimate depends on the plan choice. This choice is carried out to minimize the generalized variance of unknown parameters estimate or to maximize the information matrix determinant. To solve this extremal problem the random search is used on the basis of on the normal distribution. This method takes into account the information on the objective function by the use of covariance matrix. This method is iterative; at each iteration the search domain is gradually contracted round the point recognized to be most promising at previous iteration. So we have self-training method (named the method with a 'memory'). The algorithm is simple and can be used for large dimension of search domain. In addition, this method is suitable for parallelization by distributing of numerical statistical tests among the processes [1, 2].",
keywords = "Covariance matrices, Linear programming, Monte Carlo methods, Parallel processing, Physics, Publishing, Search problems",
author = "Ludmila Vladimirova and Irina Fatyanova",
year = "2015",
month = nov,
day = "30",
doi = "10.1109/SCP.2015.7342140",
language = "English",
pages = "361--363",
booktitle = "2015 International Conference on {"}Stability and Control Processes{"} in Memory of V.I. Zubov, SCP 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "International Conference on {"}Stability and Control Processes{"} in Memory of V.I. Zubov, SCP 2015 ; Conference date: 05-10-2015 Through 09-10-2015",
url = "http://www.apmath.spbu.ru/scp2015/openconf.php",

}

RIS

TY - GEN

T1 - Construction of regression experiment optimal plan using parallel computing

AU - Vladimirova, Ludmila

AU - Fatyanova, Irina

PY - 2015/11/30

Y1 - 2015/11/30

N2 - In this paper, we consider the classical linear regression of the second order, the unknown parameters are usually evaluated by the method of least squares. The distribution of the error of parameter vector estimate depends on the plan choice. This choice is carried out to minimize the generalized variance of unknown parameters estimate or to maximize the information matrix determinant. To solve this extremal problem the random search is used on the basis of on the normal distribution. This method takes into account the information on the objective function by the use of covariance matrix. This method is iterative; at each iteration the search domain is gradually contracted round the point recognized to be most promising at previous iteration. So we have self-training method (named the method with a 'memory'). The algorithm is simple and can be used for large dimension of search domain. In addition, this method is suitable for parallelization by distributing of numerical statistical tests among the processes [1, 2].

AB - In this paper, we consider the classical linear regression of the second order, the unknown parameters are usually evaluated by the method of least squares. The distribution of the error of parameter vector estimate depends on the plan choice. This choice is carried out to minimize the generalized variance of unknown parameters estimate or to maximize the information matrix determinant. To solve this extremal problem the random search is used on the basis of on the normal distribution. This method takes into account the information on the objective function by the use of covariance matrix. This method is iterative; at each iteration the search domain is gradually contracted round the point recognized to be most promising at previous iteration. So we have self-training method (named the method with a 'memory'). The algorithm is simple and can be used for large dimension of search domain. In addition, this method is suitable for parallelization by distributing of numerical statistical tests among the processes [1, 2].

KW - Covariance matrices

KW - Linear programming

KW - Monte Carlo methods

KW - Parallel processing

KW - Physics

KW - Publishing

KW - Search problems

UR - http://www.scopus.com/inward/record.url?scp=84960121716&partnerID=8YFLogxK

U2 - 10.1109/SCP.2015.7342140

DO - 10.1109/SCP.2015.7342140

M3 - Conference contribution

AN - SCOPUS:84960121716

SP - 361

EP - 363

BT - 2015 International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Conference on "Stability and Control Processes" in Memory of V.I. Zubov, SCP 2015

Y2 - 5 October 2015 through 9 October 2015

ER -

ID: 11351683