Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Valery A. Kalyagin"'
Publikováno v:
Journal of Statistical Planning and Inference. 201:32-39
A Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. Gaussian graphical model selection is a statistical problem that identifies the Gaussian graphical model from observations. There are s
Publikováno v:
SpringerBriefs in Optimization ISBN: 9783030602925
In this chapter, we use the general approach developed in previous chapters to analyze uncertainty of market network structure identification. The results of the Chap. 4 are illustrated by numerical experiments using the data from different stock mar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39666610e5e131f05001514e575cd51e
https://doi.org/10.1007/978-3-030-60293-2_7
https://doi.org/10.1007/978-3-030-60293-2_7
Publikováno v:
SpringerBriefs in Optimization ISBN: 9783030602925
In this chapter, we develop a theoretical foundation to study uncertainty of network structure identification algorithms. We suggest to consider identification algorithms as multiple decision statistical procedures. In this framework, uncertainty is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1bd72bf0d0241b0f84149de29660c21
https://doi.org/10.1007/978-3-030-60293-2_4
https://doi.org/10.1007/978-3-030-60293-2_4
Publikováno v:
SpringerBriefs in Optimization ISBN: 9783030602925
In this chapter, we give basic definitions related to random variable networks. After rigorous definitions of a random variable network and a network model, we give definitions of specific network structures: maximum spanning tree, planar maximally f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::920d7abe5a2f618de4792433f29b3db6
https://doi.org/10.1007/978-3-030-60293-2_2
https://doi.org/10.1007/978-3-030-60293-2_2
Publikováno v:
SpringerBriefs in Optimization ISBN: 9783030602925
In this chapter, we discuss optimality of network structure identification algorithms. We introduce a concept of optimality in the sense of minimization of the risk function. We investigate optimality of identification procedures for two problems: co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21beebcd08afbbf51b96ab8254bd18df
https://doi.org/10.1007/978-3-030-60293-2_6
https://doi.org/10.1007/978-3-030-60293-2_6
Publikováno v:
SpringerBriefs in Optimization ISBN: 9783030602925
In this chapter, we discuss robustness of network structure identification algorithms. We understand robustness of identification algorithm as the stability of the risk function with respect to the distribution of the vector X from some class of dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19d4f969ab785e4cebd186a374c0981a
https://doi.org/10.1007/978-3-030-60293-2_5
https://doi.org/10.1007/978-3-030-60293-2_5
Publikováno v:
Journal of Statistical Planning and Inference. 181:30-40
A class of distribution free multiple decision statistical procedures is proposed for threshold graph identification in correlation networks. The decision procedures are based on simultaneous application of sign statistics. It is proved that single s
Publikováno v:
Journal of the New Economic Association. 35:33-52
Network (graphical) model of stock market is a complete weighted graph. Nodes of the graph corresponds to the stocks and weights of edges are given by some measure of dependence between characteristics of the stocks. The most common characteristic of
Autor:
Valery A. Kalyagin, Panos M. Pardalos, Mario Rosario Guarracino, Ichcha Manipur, Lucia Maddalena, Ilaria Granata
Publikováno v:
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2688–2693, Madrid, 2018/12/03
info:cnr-pdr/source/autori:I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos/congresso_nome:2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)/congresso_luogo:Madrid/congresso_data:2018%2F12%2F03/anno:2018/pagina_da:2688/pagina_a:2693/intervallo_pagine:2688–2693
BIBM
info:cnr-pdr/source/autori:I. Granata, M.R. Guarracino, V. Kalyagin, L. Maddalena, I. Manipur, and P. Pardalos/congresso_nome:2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)/congresso_luogo:Madrid/congresso_data:2018%2F12%2F03/anno:2018/pagina_da:2688/pagina_a:2693/intervallo_pagine:2688–2693
BIBM
Networks represent a convenient model for many scientific and technological problems. From power grids to biological processes and functions, from financial networks to chemical compounds, the representation of case studies with graphs enables the po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eed18c3ebd685e8a0b2aa147de2c253
https://hdl.handle.net/11580/84955
https://hdl.handle.net/11580/84955
Publikováno v:
Approximation and Optimization ISBN: 9783030127664
Portfolio selection is construction of portfolios that maximize level of the expected returns from investments, but at the same time have low involved risks. One fundamental approach for quantifying the risk–return trade-off of assets is mean–var
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71d36ae41e407c71c2fa9a6901c3eb56
https://doi.org/10.1007/978-3-030-12767-1_9
https://doi.org/10.1007/978-3-030-12767-1_9