Density Based Regression for Inhomogeneous Data: Application to Lottery Experiments
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References listed on IDEAS
- Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
- Krzysztof Kontek, 2009. "Lottery valuation using the aspiration / relative utility function," Working Papers 39, Department of Applied Econometrics, Warsaw School of Economics.
- Ulrich Schmidt & Stefan Traub, 2009.
"An Experimental Investigation of the Disparity Between WTA and WTP for Lotteries,"
Theory and Decision,
Springer, vol. 66(3), pages 229-262, March.
- Traub, Stefan & Schmidt, Ulrich, 2006. "An Experimental Investigation of the Disparity between WTA and WTP for Lotteries," Economics Working Papers 2006-09, Christian-Albrechts-University of Kiel, Department of Economics.
- Schmidt, Ulrich & Traub, Stefan, 2009. "An experimental investigation of the disparity between WTA and WTP for lotteries," Open Access Publications from Kiel Institute for the World Economy 28786, Kiel Institute for the World Economy (IfW).
- Kontek, Krzysztof, 2010. "Mean, Median or Mode? A Striking Conclusion From Lottery Experiments," MPRA Paper 21758, University Library of Munich, Germany.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Kontek, Krzysztof, 2010. "Estimation of Peaked Densities Over the Interval [0,1] Using Two-Sided Power Distribution: Application to Lottery Experiments," MPRA Paper 22378, University Library of Munich, Germany.
- Kontek, Krzysztof, 2010. "Multi-Outcome Lotteries: Prospect Theory vs. Relative Utility," MPRA Paper 22947, University Library of Munich, Germany.
More about this item
KeywordsDensity Distribution; Least Squares; Quantile; Median; Mode; Maximum Likelihood Estimators; Lottery experiments; Relative Utility Function; Prospect Theory.;
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
- C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
- D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2010-05-02 (All new papers)
- NEP-ECM-2010-05-02 (Econometrics)
- NEP-UPT-2010-05-02 (Utility Models & Prospect Theory)
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