Zobrazeno 1 - 10
of 11 536
pro vyhledávání: '"Kuosmanen A"'
We study how efficient resource reallocation across cities affects potential aggregate growth. Using optimal resource allocation models and data on 284 China's prefecture-level cities in the years 2003--2019, we quantitatively measure the cost of mis
Externí odkaz:
http://arxiv.org/abs/2410.04918
Autor:
Holmen, Rasmus Bøgh1,2 klr@toi.no, Kuosmanen, Timo3, Masso, Jaan4, Maurseth, Per Botolf5, Rødseth, Kenneth Løvold6
Publikováno v:
University of Tartu - Faculty of Economics & Business Administration Working Paper Series. 2024, Issue 149, p3-30. 28p.
Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop qua
Externí odkaz:
http://arxiv.org/abs/2311.06590
Autor:
Zhou, Xun, Kuosmanen, Timo
Understanding input substitution and output transformation possibilities is critical for efficient resource allocation and firm strategy. There are important examples of fixed proportion technologies where certain inputs are non-substitutable and/or
Externí odkaz:
http://arxiv.org/abs/2404.12462
Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and many other areas. However, it has been empirically observed that conve
Externí odkaz:
http://arxiv.org/abs/2404.09528
Autor:
Koehler, Robert
Publikováno v:
Cinéaste, 2018 Apr 01. 43(2), 50-52.
Externí odkaz:
https://www.jstor.org/stable/44709624
Autor:
Kuosmanen, Timo, Dai, Sheng
Modeling of joint production has proved a vexing problem. This paper develops a radial convex nonparametric least squares (CNLS) approach to estimate the input distance function with multiple outputs. We document the correct input distance function t
Externí odkaz:
http://arxiv.org/abs/2311.11637
Autor:
Päivi Kuosmanen
Publikováno v:
Ennen ja Nyt: Historian Tietosanomat, Iss 1 (2014)
Externí odkaz:
https://doaj.org/article/a2fdef9d07314afda796700bdb886b44
Autor:
Kuosmanen, Natalia1 (AUTHOR), Kuosmanen, Timo2 (AUTHOR) timo.kuosmanen@utu.fi
Publikováno v:
Journal of Productivity Analysis. Apr2024, Vol. 61 Issue 2, p107-120. 14p.
Autor:
Kriuchkov, Iaroslav, Kuosmanen, Timo
Recent advances in operations research and machine learning have revived interest in solving complex real-world, large-size traffic control problems. With the increasing availability of road sensor data, deterministic parametric models have proved in
Externí odkaz:
http://arxiv.org/abs/2305.17517