Results 211 to 220 of about 91,178 (258)
The spatial correlation network and formation mechanism of water resource utilisation efficiency in cities in the Yellow River Basin. [PDF]
Chang W, Wang C, Liu P, Lian M.
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Differential Assembly of Core and Non-Core Host-Microbe Network Structures Along a Land-Use Change Gradient. [PDF]
Markfeld M +8 more
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Evaluating the technical efficiency of primary health facilities in delivering adolescent mental, sexual and reproductive health services in Burkina Faso. [PDF]
Tapsoba LDG +10 more
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Modeling ROI in chronic disease management: a simulation-based framework integrating patient adherence and policy timing. [PDF]
Cha J, Cha ED, Yoo EH, Song H.
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Acoustic Metasurface for Space-time Reflection Manipulation. [PDF]
Yang Y +6 more
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Dynamic quantile stochastic frontier models
International Journal of Hospitality Management, 2020This paper introduces the concept of dynamic quantile regression to the context of stochastic frontier models. We develop a Dynamic Quantile Stochastic Frontier (DQSF) in a Bayesian framework to take into account possible shifts of production (i.e. outputs) over time.
Assaf, A. George +2 more
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2000
Modern textbook presentations of production economics typically treat producers as successful optimizers. Conventional econometric practice has generally followed this paradigm, and least squares based regression techniques have been used to estimate production, cost, profit and other functions.
Kumbhakar, Subal C., Lovell, C.A. Knox
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Modern textbook presentations of production economics typically treat producers as successful optimizers. Conventional econometric practice has generally followed this paradigm, and least squares based regression techniques have been used to estimate production, cost, profit and other functions.
Kumbhakar, Subal C., Lovell, C.A. Knox
openaire +1 more source
Compression in stochastic frontier models
Annals of Tourism Research, 2021•We develop a compressed SF model to account for heterogeneity.•We allow for cross-sectional and time-series variation in all coefficients.•We use Bayesian Compression to reduce the dimensionality of the parameter space.
Mike G. Tsionas, A. George Assaf
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