Relevant bibliographies by topics / Probability weighting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Probability weighting'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Probability weighting"

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Wang, Di. "Attention-driven probability weighting." Economics Letters 203 (June 2021): 109838. http://dx.doi.org/10.1016/j.econlet.2021.109838.

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Prelec, Drazen. "The Probability Weighting Function." Econometrica 66, no. 3 (1998): 497. http://dx.doi.org/10.2307/2998573.

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Budescu, David, Ali Abbas, and Lijuan Wu. "Does probability weighting matter in probability elicitation?" Journal of Mathematical Psychology 55, no. 4 (2011): 320–27. http://dx.doi.org/10.1016/j.jmp.2011.04.002.

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Charupat, Narat, Richard Deaves, Travis Derouin, Marcelo Klotzle, and Peter Miu. "Emotional balance and probability weighting." Theory and Decision 75, no. 1 (2012): 17–41. http://dx.doi.org/10.1007/s11238-012-9348-x.

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Blavatskyy, Pavlo. "Probability weighting and L-moments." European Journal of Operational Research 255, no. 1 (2016): 103–9. http://dx.doi.org/10.1016/j.ejor.2016.05.007.

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Halpern, Elkan F. "Behind the Numbers: Inverse Probability Weighting." Radiology 271, no. 3 (2014): 625–28. http://dx.doi.org/10.1148/radiol.14140035.

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Spalt, Oliver G. "Probability Weighting and Employee Stock Options." Journal of Financial and Quantitative Analysis 48, no. 4 (2013): 1085–118. http://dx.doi.org/10.1017/s0022109013000380.

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AbstractThis paper documents that riskier firms with higher idiosyncratic volatility grant more stock options to nonexecutive employees. Standard models in the literature cannot easily explain this pattern; a model in which a risk-neutral firm and an employee with prospect theory preferences bargain over the employee's pay package can. The key feature which makes stock options attractive is probability weighting. The model fits the data on option grants well when calibrated using standard parameters from the experimental literature. The results are the first evidence that risky firms can profitably use stock options to cater to an employee demand for long-shot bets.
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Blau, Benjamin M., R. Jared DeLisle, and Ryan J. Whitby. "Does Probability Weighting Drive Lottery Preferences?" Journal of Behavioral Finance 21, no. 3 (2019): 233–47. http://dx.doi.org/10.1080/15427560.2019.1672167.

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Takahashi, Taiki. "Psychophysics of the probability weighting function." Physica A: Statistical Mechanics and its Applications 390, no. 5 (2011): 902–5. http://dx.doi.org/10.1016/j.physa.2010.10.004.

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Skinner, C. J., and D'arrigo. "Inverse probability weighting for clustered nonresponse." Biometrika 98, no. 4 (2011): 953–66. http://dx.doi.org/10.1093/biomet/asr058.

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Dissertations / Theses on the topic "Probability weighting"

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Ungemach, Christoph. "The weight of experience : an investigation of probability weighting under decisions from experience." Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/2382/.

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In decisions from experience tasks objective information regarding payoffs and probabilities must be inferred from samples of possible outcomes. A series of recent experiments has revealed that people show deviating choice behaviour in such tasks, indicating underweighting of small probabilities instead of overweighting of small probabilities as in decisions from description. In a range of experiments, the research presented in this thesis provides a new direction by showing that such reversals from overweighting to underweighting in decisions from experience are very robust and can be replicated even if all the existing explanations - sampling error, recency weighting and judgement error - are experimentally controlled for. Furthermore, reversals were replicated within common decision making biases like the common ratio effect. An important, but unexpected, new finding has been the observation of a reversed reflection effect under decisions from experience. This suggests that the difference between choice behaviour may not be restricted to underlying transformations of probabilities, as suggested in the literature. Drawing from an extensive range of model tests and parameter estimations, it is also demonstrated that the differences are reflected in the best fitting parameter values for prospect theory under decisions from experience. However, it is also shown that simple reinforcement models, which provide a more intuitive rationale for experiential choice behaviour, can account for the data just as well, without any assumptions regarding the weighting of probabilities.
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Johnson, Gregory K. "The Optimal Weighting of Pre-Election Polling Data." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2378.pdf.

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Liu, Yang. "Analysis of Dependently Truncated Sample Using Inverse Probability Weighted Estimator." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/110.

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Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.
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Wickham, Philip Arthur. "An empirical evaluation of Prelec's compound invariant functions as models of probability weighting in prospect theory." Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486090.

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Prospect theory, which proposes both reference-dependent utilityand probability weighting, has proved to be the most successful ofseveral approaches to generalising nonnative expected utility theory. It has been widely adopted in the social sciences to explain risk behaviour, with applications ranging from infrahuman, pre-industrial, financial, corporate and political decision-making. This wide, and growing, range of applications demands effective econometric analysis ofthe prospect theoretical framework. A major advance has been Prelec's approach using a weak consistency condition known as compound invariance (Cn as an axiomatic basis for developing exponential functions that describe probability weighting. Combined .with a standardly adopted constant relative risk aversion (CRRA) utility function this leads to a CI+CRRA model. Prelec proposl?s two versions ofthis model, one in which probability weighting is described by two (a probability sensitivity and a probability attractiveness) parameter and a one-parameter version in which the probability attractiveness parameter is axiomatically eliminated. One mathematical convenience ofthis version ofthe model is that it may be linearised via a double log transfonnation to provide risk decision descriptive parameters from the plot's intercept ~d gradient of certainty equivalence ~aluations. The research uses standard laboratory gambles (under - , varied incentive and problem presentation conditions) as the basis oftesting this model and the predictions ofprospect theory relating to risk attitude, pattern ofprobability weighting, gain-loss risk attitude reflection, four-fold inversion ofrisk attitude, loss aversion and framing effects. The research, involving over 12,000 individual prospect valuations generally finds strongly for prospect theory predictions. However, the two-parameter version of the CI+CRRA model performs far better as a predictor ofsubjects' prospect valuation than the one-parameter version. This is largely due to the increasing subadditivity ofvaluation as outcome value increases. Consequently the results draw into question the multiplicative assumption in prospect theory that outcome value and probability weighting are psychometrically independent. The dissertation concludes with considerations on how the model might be developed and applied to ecological risk decision-making studies in the future.
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Afonso, Lutcy Menezes. "Correcting for attrition in panel data using inverse probability weighting : an application to the european bank system." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/8155.

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Mestrado em Econometria Aplicada e Previsão<br>Esta dissertação analiza técnicas de correção do efeito do enviesamento que pode ocorrer no caso dos dados utilizados apresentarem valores em falta. Tais técnicas serão aplicadas a um modelo económico para caracterização da margem líquida de juros (MLJ) bancária, utilizando dados provinientes 15 países que pertencem ao sistema bancário da União Europeia (UE15). As variáveis que caracterizam os bancos são observados entre de 2004 e 2010. E são escolhidas seguindo Valverde et al. (2007). Adicionalmente aos regressores são acrescentadas algumas variáveis macroeconómicas. A seleção proviniente da falta de alguns valores para os regressores é tratada através da ponderação probabilistica inversa. Os ponderadores são aplicados a estimadores GMM para um modelo de dados de painel dinámico.<br>This thesis discusses techniques to correct for the potentially biasing effects of missing data. We apply the techniques on an economic model that explains the Net Interest margin (NIM) of banks, using data from 15 countries that are part of the European Union (EU15) banking system. The variables that describe banks cover the period 2004 and 2010. We use the variables that were also used in Carbó-Valverde and Fernndez (2007). In addition, also macroeconomic variables are used as regressors. The selection that occurs as a consequence of missing values in these regressor variables is dealt with by means of Inverse Probability Weighting (IPW) techniques. The weights are applied to a GMM estimator for a dynamic panel data model that would have been consistent in the absence of missing data.
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Nåtman, Jonatan. "The performance of inverse probability of treatment weighting and propensity score matching for estimating marginal hazard ratios." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385502.

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Propensity score methods are increasingly being used to reduce the effect of measured confounders in observational research. In medicine, censored time-to-event data is common. Using Monte Carlo simulations, this thesis evaluates the performance of nearest neighbour matching (NNM) and inverse probability of treatment weighting (IPTW) in combination with Cox proportional hazards models for estimating marginal hazard ratios. Focus is on the performance for different sample sizes and censoring rates, aspects which have not been fully investigated in this context before. The results show that, in the absence of censoring, both methods can reduce bias substantially. IPTW consistently had better performance in terms of bias and MSE compared to NNM. For the smallest examined sample size with 60 subjects, the use of IPTW led to estimates with bias below 15 %. Since the data were generated using a conditional parametrisation, the estimation of univariate models violates the proportional hazards assumption. As a result, censoring the data led to an increase in bias.
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Newell, Anthony. "A behavioural and biological study of risk and ambiguity." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205647/1/Anthony_Newell_Thesis.pdf.

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This thesis focuses on how individuals and groups perceive and behave in the face of risky and ambiguous outcomes. There has been much research on how individuals behave in these situations, but much less investigation into when and why they behave in this way. These questions are addressed from a behavioural, neuro-biological and environmental perspective and answered using individual and group lab-based experiments. The results of these experiments indicate that the amount of information presented, prevailing sentiment and biological factors all influence how we make decisions and the extent to which these decisions are "logical".
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Charles-Cadogan, Godfrey. "Essays on statistical economics with applications to financial market instability, limit distribution of loss aversion, and harmonic probability weighting functions." Doctoral thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20949.

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This dissertation is comprised of four essays. It develops statistical models of decision making in the presence of risk with applications to economics and finance. The methodology draws upon economics, finance, psychology, mathematics and statistics. Each essay contributes to the literature by either introducing new theories and empirical predictions or extending old ones with novel approaches .The first essay (Chapter II) includes, to the best of our knowledge, the first known limit distribution of the myopic loss aversion (MLA) index derived from micro-foundations of behavioural economics. That discovery predicts several new results. We prove that the MLA index is in the class of α-stable distributions. This striking prediction is upheld empirically with data from a published meta-study on loss aversion; published data on cross-country loss aversion indexes; and macroeconomic loss aversion index data for US and South Africa. The latter results provide contrast to Hofstede's cross-cultural uncertainty avoidance index for risk perception. We apply the theory to information based asset pricing and show how the MLA index mimics information flows in credit risk models. We embed the MLA index in the pricing kernel of a behavioural consumption based capital asset pricing model (B-CCAPM) and resolve the equity premium puzzle. Our theory predicts: (1) stochastic dominance of good states in the B-CCAPM Markov matrix induce excess volatility; and (2) a countercyclical fourfold pattern of risk attitudes. The second essay (Chapter III) introduces a probability model of "irrational exuberance "and financial market instability implied by index option prices. It is based on a behavioural empirical local Lyapunov exponent (BELLE) process we construct from micro-foundations of behavioural finance. It characterizes stochastic stability of financial markets, with risk attitude factors in fixed point neighbourhoods of the probability weighting functions implied by index option prices. It provides a robust early warning system for market crash across different credit risk sources. We show how the model would have predicted the Great Recession of 2008. The BELLE process characterizes Minskys financial instability hypothesis that financial markets transit from financial relations that make them stable to those that make them unstable.
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Diop, Serigne Arona, and Serigne Arona Diop. "Comparing inverse probability of treatment weighting methods and optimal nonbipartite matching for estimating the causal effect of a multicategorical treatment." Master's thesis, Université Laval, 2019. http://hdl.handle.net/20.500.11794/34507.

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Des débalancements des covariables entre les groupes de traitement sont souvent présents dans les études observationnelles et peuvent biaiser les comparaisons entre les traitements. Ce biais peut notamment être corrigé grâce à des méthodes de pondération ou d’appariement. Ces méthodes de correction ont rarement été comparées dans un contexte de traitement à plusieurs catégories (>2). Nous avons mené une étude de simulation pour comparer une méthode d’appariement optimal non-biparti, la pondération par probabilité inverse de traitement ainsi qu’une pondération modifiée analogue à l’appariement (matching weights). Ces comparaisons ont été effectuées dans le cadre de simulation de type Monte Carlo à travers laquelle une variable d’exposition à 3 groupes a été utilisée. Une étude de simulation utilisant des données réelles (plasmode) a été conduite et dans laquelle la variable de traitement avait 5 catégories. Parmi toutes les méthodes comparées, celle du matching weights apparaît comme étant la plus robuste selon le critère de l’erreur quadratique moyenne. Il en ressort, aussi, que les résultats de la pondération par probabilité inverse de traitement peuvent parfois être améliorés par la troncation. De plus, la performance de la pondération dépend du niveau de chevauchement entre les différents groupes de traitement. La performance de l’appariement optimal nonbiparti est, quant à elle, fortement tributaire de la distance maximale pour qu’une paire soit formée (caliper). Toutefois, le choix du caliper optimal n’est pas facile et demeure une question ouverte. De surcroît, les résultats obtenus avec la simulation plasmode étaient positifs, dans la mesure où une réduction importante du biais a été observée. Toutes les méthodes ont pu réduire significativement le biais de confusion. Avant d’utiliser la pondération de probabilité inverse de traitement, il est recommandé de vérifier la violation de l’hypothèse de positivité ou l’existence de zones de chevauchement entre les différents groupes de traitement<br>Des débalancements des covariables entre les groupes de traitement sont souvent présents dans les études observationnelles et peuvent biaiser les comparaisons entre les traitements. Ce biais peut notamment être corrigé grâce à des méthodes de pondération ou d’appariement. Ces méthodes de correction ont rarement été comparées dans un contexte de traitement à plusieurs catégories (>2). Nous avons mené une étude de simulation pour comparer une méthode d’appariement optimal non-biparti, la pondération par probabilité inverse de traitement ainsi qu’une pondération modifiée analogue à l’appariement (matching weights). Ces comparaisons ont été effectuées dans le cadre de simulation de type Monte Carlo à travers laquelle une variable d’exposition à 3 groupes a été utilisée. Une étude de simulation utilisant des données réelles (plasmode) a été conduite et dans laquelle la variable de traitement avait 5 catégories. Parmi toutes les méthodes comparées, celle du matching weights apparaît comme étant la plus robuste selon le critère de l’erreur quadratique moyenne. Il en ressort, aussi, que les résultats de la pondération par probabilité inverse de traitement peuvent parfois être améliorés par la troncation. De plus, la performance de la pondération dépend du niveau de chevauchement entre les différents groupes de traitement. La performance de l’appariement optimal nonbiparti est, quant à elle, fortement tributaire de la distance maximale pour qu’une paire soit formée (caliper). Toutefois, le choix du caliper optimal n’est pas facile et demeure une question ouverte. De surcroît, les résultats obtenus avec la simulation plasmode étaient positifs, dans la mesure où une réduction importante du biais a été observée. Toutes les méthodes ont pu réduire significativement le biais de confusion. Avant d’utiliser la pondération de probabilité inverse de traitement, il est recommandé de vérifier la violation de l’hypothèse de positivité ou l’existence de zones de chevauchement entre les différents groupes de traitement
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KATO, Ryo, and Dan HU. "Auditor Size as a Measure for Audit Quality : A Japanese Study." 名古屋大学大学院経済学研究科附属国際経済政策研究センター, 2014. http://hdl.handle.net/2237/20455.

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Books on the topic "Probability weighting"

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Canada, Statistics. Sampling and weighting. Ministry of Supply and Services, 1999.

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Canada, Statistics, ed. Sampling and weighting. Statistics Canada, 1999.

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Sampling and weighting: 1991 Census technical report. Statistics Canada, 1991.

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Canada, Statistics. Sampling and weighting: 1996 Census technical report. Statistics Canada, 1996.

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Barberis, Nicholas. Stocks as lotteries: The implications of probability weighting for security prices. National Bureau of Economic Research, 2007.

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Pavlenko, Tatijana. Asymptotic behavior of the probabilities misclassification for discriminant functions with weighting. University of Toronto, 1997.

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Canada, Statistics, ed. Sampling and weighting. Statistics Canada, 1994.

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Smithson, Michael. Human Understandings of Probability. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.29.

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In this chapter developments in theories and research on human understandings and judgements of probability are examined. The concept of probability as a degree of belief and the systematic study of human probability judgements have emerged only recently, but have stimulated numerous fruitful debates about the nature of rationality, belief formation, decision-making, and uncertainty itself. The chapter begins with a review of how the connection between probability and degrees of belief was developed and elaborated to form a prescriptive framework, followed by a brief summary of debates concerning rationality and uncertainty. It then surveys models of human probability judgements based on probability weighting functions, ways in which these judgements depend on how relevant information is presented, mental shortcuts (or “heuristics”) underpinning such judgements, the extent to which probability judgements are miscalibrated, fallacies in judgements of probabilities of compound and conditional events, and debates concerning the effective communication of probabilistic information.
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Byker, Tanya, and Italo Gutierrez. Treatment Effects Using Inverse Probability Weighting and Contaminated Treatment Data: An Application to the Evaluation of a Government Female Sterilization Campaign in Peru. RAND Corporation, 2016. http://dx.doi.org/10.7249/wr1118-1.

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Book chapters on the topic "Probability weighting"

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Husmeier, Dirk. "Network Committees and Weighting Schemes." In Neural Networks for Conditional Probability Estimation. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0847-4_13.

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Hota, Ashish R., and Shreyas Sundaram. "Interdependent Security Games Under Behavioral Probability Weighting." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25594-1_9.

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Nardon, Martina, and Paolo Pianca. "A Note on the Shape of the Probability Weighting Function." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_85.

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Wei, Yan, and Xiao Wu. "Fuzzy Support Vector Machine based on the Posterior Probability Weighting Membership." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_16.

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Nardon, Martina, and Paolo Pianca. "The Effects of Curvature and Elevation of the Probability Weighting Function on Options Prices." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05014-0_35.

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Wahid, Abdul, Xiaoying Gao, and Peter Andreae. "A Soft Subspace Clustering Method for Text Data Using a Probability Based Feature Weighting Scheme." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26187-4_9.

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Zhang, Ying, and Mei-Jie Zhang. "Inference of Transition Probabilities in Multi-State Models Using Adaptive Inverse Probability Censoring Weighting Technique." In Statistical Modeling in Biomedical Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33416-1_19.

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Kudowa, Evaristar N., and Mavuto F. Mukaka. "Application of Multiple Imputation, Inverse Probability Weighting, and Double Robustness in Determining Blood Donor Deferral Characteristics in Malawi." In Modern Biostatistical Methods for Evidence-Based Global Health Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11012-2_18.

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Breslow, Norman E., and Noel Weiss. "Inverse Probability Weighting in Nested Case-Control Studies." In Handbook of Statistical Methods for Case-Control Studies. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315154084-19.

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Kott, Phillip S. "Calibration Weighting: Combining Probability Samples and Linear Prediction Models." In Handbook of Statistics. Elsevier, 2009. http://dx.doi.org/10.1016/s0169-7161(09)00225-9.

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Conference papers on the topic "Probability weighting"

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Cockburn, Andy, Philip Quinn, Carl Gutwin, Zhe Chen, and Pang Suwanaposee. "Probability Weighting in Interactive Decisions." In CHI '22: CHI Conference on Human Factors in Computing Systems. ACM, 2022. http://dx.doi.org/10.1145/3491102.3517477.

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Li, Yanlai, and Sheng Wu. "Prospect Theory: A Novel Probability Weighting Function Model." In 2014 International Conference of Logistics Engineering and Management. American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413753.143.

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Abdallah, Mustafa, Parinaz Naghizadeh, Timothy Cason, Saurabh Bagchi, and Shreyas Sundaram. "Protecting Assets with Heterogeneous Valuations under Behavioral Probability Weighting." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9030279.

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Kerr, M., Marta Burgay, Nicolò D’Amico, Paolo Esposito, Alberto Pellizzoni, and Andrea Possenti. "Detecting Weak Gamma-ray Pulsars with Photon Probability Weighting." In RADIO PULSARS: AN ASTROPHYSICAL KEY TO UNLOCK THE SECRETS OF THE UNIVERSE. AIP, 2011. http://dx.doi.org/10.1063/1.3615077.

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Zhou, Xiaofeng, Jimmy Xiangji Huang, and Ben He. "Enhancing ad-hoc relevance weighting using probability density estimation." In the 34th international ACM SIGIR conference. ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2009943.

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Chu, Na, Lizhuang Ma, Min Zhou, Yiyang Hu, Ping Liu, and Zhiying Che. "Attribute weighting with probability estimation trees for improving probability-based ranking in liver diagnosis." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703900.

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Lenhart, James H., and Scott A. de Ridder. "Weighting of Field Monitoring Data with Probability Distributions of Daily Rainfall." In World Environmental and Water Resources Congress 2008. American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)9.

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Sun, Cunwu, Hongqing Ye, Wei Weng, and Qingwei Wang. "Two-Stage Newsvendor Problem with Loss Aversion and Probability Weighting Effects." In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2020. http://dx.doi.org/10.1109/ieem45057.2020.9309893.

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Abdallah, Mustafa, Parinaz Naghizadeh, Ashish R. Hota, Timothy Cason, Saurabh Bagchi, and Shreyas Sundaram. "The Impacts of Behavioral Probability Weighting on Security Investments in Interdependent Systems." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814307.

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Abdallah, Mustafa, Timothy Cason, Saurabh Bagchi, and Shreyas Sundaram. "The Effect of Behavioral Probability Weighting in a Sequential Defender-Attacker Game." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9304311.

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Reports on the topic "Probability weighting"

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Dimmock, Stephen, Roy Kouwenberg, Olivia Mitchell, and Kim Peijnenburg. Household Portfolio Underdiversification and Probability Weighting: Evidence from the Field. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24928.

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Barberis, Nicholas, and Ming Huang. Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. National Bureau of Economic Research, 2007. http://dx.doi.org/10.3386/w12936.

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Dutra, Lauren M., Matthew C. Farrelly, Brian Bradfield, Jamie Ridenhour, and Jamie Guillory. Modeling the Probability of Fraud in Social Media in a National Cannabis Survey. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0046.2109.

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Abstract:
Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report outlines two techniques that we used to problem-solve several challenges with the resulting data: (1) developing a model for detecting fraudulent cases in social media completes after standard fraud detection measures were insufficient and (2) designing a weighting scheme to pool multiple probability and nonprobability samples. We also describe our approach for validating the pooled dataset. The fraud prevention and detection processes, predictive model of fraud, and the methods used to weight the probability and nonprobability samples can be applied to current and future complex data collections and analysis of existing datasets.
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Schling, Maja, and Nicolás Pazos. El impacto de subsidios inteligentes en la producción agrícola: evidencia innovadora de Argentina utilizando datos de encuesta y de teledetección. Banco Interamericano de Desarrollo, 2022. http://dx.doi.org/10.18235/0004352.

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Abstract:
Este estudio evalúa el impacto del Programa de Desarrollo Rural y Agricultura Familiar (PRODAF), un proyecto de subsidios inteligentes que benefició al estrato de agricultores familiares en el noreste de Argentina. La evaluación aprovecha dos fuentes complementarias de datos. La primera es una encuesta de hogares agrícolas con una muestra de 898 productores (534 tratados y 364 control) después de la finalización del proyecto. La segunda utiliza la georreferenciación de las parcelas para medir los rendimientos agrícolas con datos satelitales para una submuestra de 195 productores durante un periodo de 10 años. Utilizando la metodología de Inverse Probability Weighting, encontramos que el PRODAF aumentó la tasa de adopción de tecnologías en 21 puntos porcentuales, e incrementó el acceso a crédito en 47 puntos porcentuales. Superar estas barreras permitió a los productores beneficiarios aumentar el valor de sus ventas e ingresos netos, aunque los impactos fueron dispares entre las cuatro cadenas priorizadas. En cambio, el análisis solo detectó un impacto significativo en los rendimientos para la cadena citrícola, lo cual potencialmente se debe al tipo de tecnología adoptada en esta cadena. Por último, construimos el ndice de Vegetación de Diferencia Normalizada (NDVI) para aproximar la productividad en las cadenas algodonera y citrícola. Aplicando el método de Event Study, confirmamos que la adopción de tecnologías es un proceso complejo que solo desarrolla su impacto completo sobre los rendimientos entre el segundo y tercer año post-tratamiento. Además, confirmamos que el uso de datos satelitales es una herramienta efectiva que estima cambios en los rendimientos con precisión y puede servir para monitorear y evaluar este tipo de intervención de forma contemporánea y a bajo costo.
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