2 résultats pour « statistical »

Do Finance Researchers Address Sample Size Issues? – A Bayesian Inquiry in the AI Era.

Traditional #statistical and #algorithm-based methods used to analyze #bigdata often overlook small but significant evidence. #bayesian #statistics, driven by #conditional #probability, offer a solution to this challenge. The review identifies two main applications of Bayesian statistics in #finance: prediction in financial markets and credit risk models. The findings aim to provide valuable insights for researchers aiming to incorporate Bayesian methods and address the sample size issue effectively in #financial #research.

Measuring Discrete Risks on Infinite Domains

Date : Tags : , , , , ,
This paper presents an extension of #statistical inference for smoothed quantile estimators from finite domains to infinite domains. A new truncation methodology is proposed for discrete loss distributions with infinite domains. #simulation studies using several distributions commonly used in the #insuranceindustry show the effectiveness of the methodology. The authors also propose a flexible bootstrap-based approach and demonstrate its use in computing the conditional five number summary (C5NS) for tail risk and constructing confidence intervals for each of the five quantiles that make up C5NS. Results using #automobile #accident #data show that the smoothed quantile approach produces more accurate classifications of tail #risk and lower coefficients of variation in the estimation of tail #probabilities compared to the linear interpolation approach.