The Experts below are selected from a list of 96948 Experts worldwide ranked by ideXlab platform
Wenjun Zhu - One of the best experts on this subject based on the ideXlab platform.
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a Relational Model for predicting farm level crop yield distributions in the absence of farm level data
Social Science Research Network, 2016Co-Authors: Lysa Porth, Ken Seng Tan, Wenjun ZhuAbstract:Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust Model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-stage Relational Model to predict farm-level crop yield distributions for a country (base country) in the absence of farm yield losses, through “borrowing” information from a neighbouring country (reference country), where detailed farm-level yield experience is available. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically validate the performance of the proposed Relational Model. Empirical results show that the approach developed in this paper can predict farm-level data accurately and hence may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.
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a Relational Model for predicting farm level crop yield distributions in the absence of farm level data
2016 Annual Meeting July 31-August 2 Boston Massachusetts, 2016Co-Authors: Lysa Porth, Ken Seng Tan, Wenjun ZhuAbstract:Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust Model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-step Relational Model to predict farm-level crop yield distributions in the absence of farm yield losses, through "borrowing" information from a neighbouring country, where detailed farm-level yield experience is available. The first step of the Relational Model defines a similarity measure based on a Euclidean metric to select an optimal county, considering weather information, average farm size, county size and county-level yield volatility. The second step links the selected county with the county to be predicted through Modeling the dependence structures between the farm-level and county-level yield losses. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically examine the performance of the proposed Relational Model. The results show that the approach developed in this paper may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.
Lysa Porth - One of the best experts on this subject based on the ideXlab platform.
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a Relational Model for predicting farm level crop yield distributions in the absence of farm level data
Social Science Research Network, 2016Co-Authors: Lysa Porth, Ken Seng Tan, Wenjun ZhuAbstract:Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust Model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-stage Relational Model to predict farm-level crop yield distributions for a country (base country) in the absence of farm yield losses, through “borrowing” information from a neighbouring country (reference country), where detailed farm-level yield experience is available. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically validate the performance of the proposed Relational Model. Empirical results show that the approach developed in this paper can predict farm-level data accurately and hence may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.
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a Relational Model for predicting farm level crop yield distributions in the absence of farm level data
2016 Annual Meeting July 31-August 2 Boston Massachusetts, 2016Co-Authors: Lysa Porth, Ken Seng Tan, Wenjun ZhuAbstract:Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust Model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-step Relational Model to predict farm-level crop yield distributions in the absence of farm yield losses, through "borrowing" information from a neighbouring country, where detailed farm-level yield experience is available. The first step of the Relational Model defines a similarity measure based on a Euclidean metric to select an optimal county, considering weather information, average farm size, county size and county-level yield volatility. The second step links the selected county with the county to be predicted through Modeling the dependence structures between the farm-level and county-level yield losses. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically examine the performance of the proposed Relational Model. The results show that the approach developed in this paper may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.
Stefan Kramer - One of the best experts on this subject based on the ideXlab platform.
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sindbad and siql an inductive database and query language in the Relational Model
European conference on Machine Learning, 2008Co-Authors: Jorg Wicker, Kristina Kessler, Lothar Richter, Stefan KramerAbstract:In this demonstration, we will present the concepts and an implementation of an inductive database--- as proposed by Imielinski and Mannila --- in the Relational Model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-Relational data, we incorporated multi-Relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification Models made it necessary to extend the data Model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
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an inductive database and query language in the Relational Model
Extending Database Technology, 2008Co-Authors: Lothar Richter, Jorg Wicker, Kristina Kessler, Stefan KramerAbstract:In the demonstration, we will present the concepts and an implementation of an inductive database -- as proposed by Imielinski and Mannila -- in the Relational Model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-Relational data, we incorporated multi-Relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification Models made it necessary to extend the data Model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
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inductive databases in the Relational Model the data as the bridge
Lecture Notes in Computer Science, 2006Co-Authors: Stefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow, Jorg Wicker, Lothar RichterAbstract:We present a new and comprehensive approach to inductive databases in the Relational Model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside Models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.
Lothar Richter - One of the best experts on this subject based on the ideXlab platform.
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sindbad and siql an inductive database and query language in the Relational Model
European conference on Machine Learning, 2008Co-Authors: Jorg Wicker, Kristina Kessler, Lothar Richter, Stefan KramerAbstract:In this demonstration, we will present the concepts and an implementation of an inductive database--- as proposed by Imielinski and Mannila --- in the Relational Model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-Relational data, we incorporated multi-Relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification Models made it necessary to extend the data Model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
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an inductive database and query language in the Relational Model
Extending Database Technology, 2008Co-Authors: Lothar Richter, Jorg Wicker, Kristina Kessler, Stefan KramerAbstract:In the demonstration, we will present the concepts and an implementation of an inductive database -- as proposed by Imielinski and Mannila -- in the Relational Model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-Relational data, we incorporated multi-Relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification Models made it necessary to extend the data Model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
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inductive databases in the Relational Model the data as the bridge
Lecture Notes in Computer Science, 2006Co-Authors: Stefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow, Jorg Wicker, Lothar RichterAbstract:We present a new and comprehensive approach to inductive databases in the Relational Model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside Models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.
Ken Seng Tan - One of the best experts on this subject based on the ideXlab platform.
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a Relational Model for predicting farm level crop yield distributions in the absence of farm level data
Social Science Research Network, 2016Co-Authors: Lysa Porth, Ken Seng Tan, Wenjun ZhuAbstract:Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust Model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-stage Relational Model to predict farm-level crop yield distributions for a country (base country) in the absence of farm yield losses, through “borrowing” information from a neighbouring country (reference country), where detailed farm-level yield experience is available. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically validate the performance of the proposed Relational Model. Empirical results show that the approach developed in this paper can predict farm-level data accurately and hence may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.
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a Relational Model for predicting farm level crop yield distributions in the absence of farm level data
2016 Annual Meeting July 31-August 2 Boston Massachusetts, 2016Co-Authors: Lysa Porth, Ken Seng Tan, Wenjun ZhuAbstract:Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust Model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-step Relational Model to predict farm-level crop yield distributions in the absence of farm yield losses, through "borrowing" information from a neighbouring country, where detailed farm-level yield experience is available. The first step of the Relational Model defines a similarity measure based on a Euclidean metric to select an optimal county, considering weather information, average farm size, county size and county-level yield volatility. The second step links the selected county with the county to be predicted through Modeling the dependence structures between the farm-level and county-level yield losses. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically examine the performance of the proposed Relational Model. The results show that the approach developed in this paper may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.
