Zobrazeno 1 - 10
of 326
pro vyhledávání: '"KUOSMANEN TIMO"'
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:
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
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:
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
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
Quantile regression and partial frontier are two distinct approaches to nonparametric quantile frontier estimation. In this article, we demonstrate that partial frontiers are not quantiles. Both convex and nonconvex technologies are considered. To th
Externí odkaz:
http://arxiv.org/abs/2205.11885
Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, th
Externí odkaz:
http://arxiv.org/abs/2204.01371
Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning and related fields. In the field of productivity and efficiency analysis, recent developments in the multivariate convex r
Externí odkaz:
http://arxiv.org/abs/2109.12962