Zobrazeno 1 - 10
of 2 289
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
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:
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:
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
Autor:
Kewang Nan, Kiwan Wong, Dengfeng Li, Binbin Ying, James C. McRae, Vivian R. Feig, Shubing Wang, Ningjie Du, Yuelong Liang, Qijiang Mao, Enjie Zhou, Yonglin Chen, Lei Sang, Kuanming Yao, Jingkun Zhou, Jian Li, Joshua Jenkins, Keiko Ishida, Johannes Kuosmanen, Wiam Abdalla Mohammed Madani, Alison Hayward, Khalil B. Ramadi, Xinge Yu, Giovanni Traverso
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Ingestible electronics have the capacity to transform our ability to effectively diagnose and potentially treat a broad set of conditions. Current applications could be significantly enhanced by addressing poor electrode-tissue contact, lack
Externí odkaz:
https://doaj.org/article/078820bf896644b285cff3b0f3feb37a
Publikováno v:
In Journal of Environmental Management May 2024 359
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss f
Externí odkaz:
http://arxiv.org/abs/2209.12538
Publikováno v:
Cell Reports Medicine, Vol 5, Iss 9, Pp 101742- (2024)
Externí odkaz:
https://doaj.org/article/7f38925763a74f50b66a2a822985c810