Results 21 to 30 of about 114,812 (338)

Probability weighting arises from boundary repulsions of cognitive noise [PDF]

Bedi S, de Hollander G, Ruff CC.
europepmc   +2 more sources

On the Source and Instability of Probability Weighting

, 2023
Cary Frydman, Lawrence Jin
openalex   +2 more sources

Long-term outcomes of proximal gastrectomy versus total gastrectomy for clinically advanced upper-third gastric cancer: an inverse probability weighting analysis. [PDF]

World J Surg Oncol
Shimoyama H, Haruta S, Fujisawa K, Ogawa Y, Ohkura Y, Ueno M, Udagawa H.   +6 more
europepmc   +3 more sources

Cumulative Prospect Theory Version with Fuzzy Values of Outcome Estimates

Risks, 2021
Choosing solutions under risk and uncertainty requires the consideration of several factors. One of the main factors in choosing a solution is modeling the decision maker’s attitude to risk. The expected utility theory was the first approach that allowed
Oleg Uzhga-Rebrov, Peter Grabusts
doaj   +1 more source

The effect of positive autobiographical memory retrieval on decision-making under risk: A computational model-based analysis

Frontiers in Psychiatry, 2022
Psychiatric disorders such as depressive and anxiety disorders are associated with altered decision-making under risk. Recent advances in neuroeconomics and computational psychiatry have further discomposed risk-based decision-making into distinct ...
Natsumi Shimizu, Yasuhiro Mochizuki, Chong Chen, Kosuke Hagiwara, Karin Matsumoto, Yusuke Oda, Masako Hirotsu, Emi Okabe, Toshio Matsubara, Shin Nakagawa   +9 more
doaj   +1 more source

Probability weighting functions [PDF]

, 2010
Expected utility (EU) theory is unable to accommodate the observed nonlinear weighting of probabilities. We outline three stylized facts on nonlinear weighting that a theory of risk must ideally address.
Ali al-Nowaihi (7585052), S. Dhami (7647767), Sanjit Dhami (7585055)   +2 more
core   +2 more sources

Inverse probability weighting [PDF]

BMJ, 2016
Statistical analysis usually treats all observations as equally important. In some circumstances, however, it is appropriate to vary the weight given to different observations. Well known examples are in meta-analysis, where the inverse variance (precision) weight given to each contributing study varies, and in the analysis of clustered data.1 ...
Mansournia, M, Altman, D
openaire   +3 more sources

Non-Asymptotic Bounds of AIPW Estimators for Means with Missingness at Random

Mathematics, 2023
The augmented inverse probability weighting is well known for its double robustness in missing data and causal inference. If either the propensity score model or the outcome regression model is correctly specified, the estimator is guaranteed to be ...
Fei Wang, Yuhao Deng
doaj   +1 more source

Comparison of Propensity Score Weighting Methods to Remove Selection Bias in Average Treatment Effect Estimates

International Journal of Turkish Education Sciences, 2023
In this Monte Carlo simulation study, the performance of six different propensity score methods implemented through weighting cases was investigated: inverse probability of treatment weighting, truncated inverse probability of treatment weighting ...
Sungur Gürel, Walter Lana Leite
doaj   +1 more source

Probability weighting function in experiment using graphically represented probability information

Journal of Human Environmental Studies, 2014
A psychological experiment was conducted to estimate probability weighting function whose stimuli were graphically presented. We conducted a modified experiment of Wu and Gonzalez (1996) study by using graphical representation of lotteries.
Hajime Murakami, Yuki Tamari, Takashi Ideno, Shigetaka Okubo, Kazuhisa Takemura   +4 more
doaj   +1 more source