The Experts below are selected from a list of 54981 Experts worldwide ranked by ideXlab platform
Efthymios G. Tsionas - One of the best experts on this subject based on the ideXlab platform.
-
Nonparametric estimation of production risk and risk Preference Functions
Advances in Econometrics, 2009Co-Authors: Subal C. Kumbhakar, Efthymios G. TsionasAbstract:This paper deals with estimation of risk and the risk Preference Function when producers face uncertainties in production (usually labeled as production risk) and output price. These uncertainties are modeled in the context of production theory where the objective of the producers is to maximize expected utility of normalized anticipated profit. Models are proposed to estimate risk Preference of individual producers under (i) only production risk, (ii) only price risk, (iii) both production and price risks, (iv) production risk with technical inefficiency, (v) price risk with technical inefficiency, and (vi) both production and price risks with technical inefficiency. We discuss estimation of the production Function, the output risk Function, and the risk Preference Functions in some of these cases. Norwegian salmon farming data is used for an empirical application of some of the proposed models. We find that salmon farmers are, in general, risk averse. Labor is found to be risk decreasing while capital and feed are found to be risk increasing.
-
Estimation of production risk and risk Preference Function : a nonparametric approach
Annals of Operations Research, 2008Co-Authors: Subal C. Kumbhakar, Efthymios G. TsionasAbstract:While estimating parametric production models with risk, one faces two main problems. The first problem is associated with the choice of Functional forms on the mean production Function and the risk (variance) Function. The second problem is associated with the specification of the risk Preference Function. In a parametric model the researcher chooses some ad hoc Functional form on all these. It is obvious that the estimated (i) technology (mean production Function), (ii) risk and (iii) risk Preference Functions are affected by the choice of Functional form. In this paper we consider an estimation framework that avoids assuming parametric Functions on all three. In particular, this paper deals with nonparametric estimation of the technology, risk and risk Preferences of producers when they face uncertainty in production. Uncertainty is modeled in the context of production theory where producers’ maximize expected utility of anticipated profit. A multi-stage nonparametric estimation procedure is used to estimate the production Function, the output risk Function and the risk Preference Function. No distributional assumption is made on the random term representing production uncertainty. No Functional form is assumed on the underlying utility Function. Rice farming data from Philippines are used for an empirical application of the proposed model. Rice farmers are, in general, found to be risk averse; labor is risk decreasing while fertilizer, land and materials are risk increasing. The mean risk premium is about 3% of mean profit.
Ivana Černá - One of the best experts on this subject based on the ideXlab platform.
-
CDC - Attraction-based receding horizon path planning with temporal logic constraints
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana ČernáAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization of the suggested framework in an illustrative example.
-
Attraction-Based Receding Horizon Path Planning with Temporal Logic Constraints
arXiv: Robotics, 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana ČernáAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. We assume that arbitrary assumptions on the reward dynamics can be given. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization and benefits of the suggested framework in an illustrative example.
Subal C. Kumbhakar - One of the best experts on this subject based on the ideXlab platform.
-
Nonparametric estimation of production risk and risk Preference Functions
Advances in Econometrics, 2009Co-Authors: Subal C. Kumbhakar, Efthymios G. TsionasAbstract:This paper deals with estimation of risk and the risk Preference Function when producers face uncertainties in production (usually labeled as production risk) and output price. These uncertainties are modeled in the context of production theory where the objective of the producers is to maximize expected utility of normalized anticipated profit. Models are proposed to estimate risk Preference of individual producers under (i) only production risk, (ii) only price risk, (iii) both production and price risks, (iv) production risk with technical inefficiency, (v) price risk with technical inefficiency, and (vi) both production and price risks with technical inefficiency. We discuss estimation of the production Function, the output risk Function, and the risk Preference Functions in some of these cases. Norwegian salmon farming data is used for an empirical application of some of the proposed models. We find that salmon farmers are, in general, risk averse. Labor is found to be risk decreasing while capital and feed are found to be risk increasing.
-
Estimation of production risk and risk Preference Function : a nonparametric approach
Annals of Operations Research, 2008Co-Authors: Subal C. Kumbhakar, Efthymios G. TsionasAbstract:While estimating parametric production models with risk, one faces two main problems. The first problem is associated with the choice of Functional forms on the mean production Function and the risk (variance) Function. The second problem is associated with the specification of the risk Preference Function. In a parametric model the researcher chooses some ad hoc Functional form on all these. It is obvious that the estimated (i) technology (mean production Function), (ii) risk and (iii) risk Preference Functions are affected by the choice of Functional form. In this paper we consider an estimation framework that avoids assuming parametric Functions on all three. In particular, this paper deals with nonparametric estimation of the technology, risk and risk Preferences of producers when they face uncertainty in production. Uncertainty is modeled in the context of production theory where producers’ maximize expected utility of anticipated profit. A multi-stage nonparametric estimation procedure is used to estimate the production Function, the output risk Function and the risk Preference Function. No distributional assumption is made on the random term representing production uncertainty. No Functional form is assumed on the underlying utility Function. Rice farming data from Philippines are used for an empirical application of the proposed model. Rice farmers are, in general, found to be risk averse; labor is risk decreasing while fertilizer, land and materials are risk increasing. The mean risk premium is about 3% of mean profit.
Maria Svorenova - One of the best experts on this subject based on the ideXlab platform.
-
CDC - Attraction-based receding horizon path planning with temporal logic constraints
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana ČernáAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization of the suggested framework in an illustrative example.
-
attraction based receding horizon path planning with temporal logic constraints
Conference on Decision and Control, 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana CernaAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization of the suggested framework in an illustrative example.
-
Attraction-Based Receding Horizon Path Planning with Temporal Logic Constraints
arXiv: Robotics, 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana ČernáAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. We assume that arbitrary assumptions on the reward dynamics can be given. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization and benefits of the suggested framework in an illustrative example.
Jana Tumova - One of the best experts on this subject based on the ideXlab platform.
-
CDC - Attraction-based receding horizon path planning with temporal logic constraints
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana ČernáAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization of the suggested framework in an illustrative example.
-
attraction based receding horizon path planning with temporal logic constraints
Conference on Decision and Control, 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana CernaAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization of the suggested framework in an illustrative example.
-
Attraction-Based Receding Horizon Path Planning with Temporal Logic Constraints
arXiv: Robotics, 2012Co-Authors: Maria Svorenova, Jana Tumova, Jiri Barnat, Ivana ČernáAbstract:Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. We assume that arbitrary assumptions on the reward dynamics can be given. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined Preference Function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization and benefits of the suggested framework in an illustrative example.
