Results 161 to 170 of about 40,175 (220)
Caution Ahead: Numerical Reasoning and Look‐Ahead Bias in AI Models
ABSTRACT Recent work within accounting and finance has highlighted that modern AI systems exhibit superhuman performance on a variety of foundational activities within these fields. However, the literature often does not provide economic rationale for why AI models seem to outperform, largely because these models are a black box.
BRADFORD LEVY
wiley +1 more source
Dynamic Pricing With Recommendation and Consumer Feedback
ABSTRACT A long‐lived seller sells a new product of unknown value by offering prices and recommendations to short‐lived consumers in continuous time. The seller receives consumer feedback about the product at a rate that increases with the instantaneous sales volume.
Wenji Xu, Shuoguang Yang
wiley +1 more source
Bayesian Decision Thresholds for Bushfire Warnings: Calibration and Robustness for Rare‐Event Risk
ABSTRACT Current bushfire warning systems communicate the probability of a warning given danger, but residents require the probability of danger given a warning. This misalignment, combined with the extreme rarity of catastrophic fires, often leads to the dangerous ‘wait and see’ behaviour.
Miodrag Lovric, Ojas Davé
wiley +1 more source
A Probabilistic Greedy Attempt to Be Fair in Neural Team Recommendation
ABSTRACT Neural team recommendation has brought state‐of‐the‐art efficacy while enhancing efficiency at forming teams of experts whose success in completing complex tasks is almost surely guaranteed. However, they overlook fairness, that is, predicted teams are heavily biased toward popular and male experts, falling short of recommending female or ...
Hamed Loghmani +4 more
wiley +1 more source
New Heuristics for Stable LDA Parameter Search
ABSTRACT The Latent Dirichlet Allocation (LDA) algorithm automatically extracts latent topics from a textual corpus, but configuring its parameters can be difficult and time‐consuming. Optimization algorithms can help determine the best parameters, but not necessarily the optimal ones.
Simon‐Olivier Harel +2 more
wiley +1 more source
A Bayesian‐Based Integrative Bioinformatics Analysis Nominates Oncogenic Drivers in Neuroblastoma
ABSTRACT Identifying targetable oncogenic drivers remains a challenge in neuroblastoma, the most common extracranial solid malignancy in children. We applied a Bayesian algorithm for integrative analysis of expression and copy‐number, iExCN, to nominate oncogenic drivers in neuroblastoma.
Lin Xu +14 more
wiley +1 more source
ABSTRACT The present paper provides an overall framework to afford the problem of non‐representativeness and non‐random selectivity arising from online job ads data, using Generalized sample selection models and Eurostat benchmark data. We jointly model the outcome intensity (number of online job ads in observed profiles, whose levels are defined by ...
Pietro Giorgio Lovaglio +1 more
wiley +1 more source
Bayesian Inference for Joint Estimation Models Using Copulas to Handle Endogenous Regressors
ABSTRACT This study proposes a Bayesian approach for finite‐sample inference of the Gaussian copula endogeneity correction. Extant studies use frequentist inference, build on a priori computed estimates of marginal distributions of explanatory variables, and use bootstrapping to obtain standard errors. The proposed Bayesian approach facilitates precise
Rouven E. Haschka
wiley +1 more source
Abstract Armed groups operating in conflicts around the world publish statements of denial to dissociate themselves from acts of violence. Existing research argues that armed groups publish denial statements to avoid public backlash, favorably frame the conduct of their campaigns, and distance themselves from unsanctioned actions conducted by rank‐and ...
Ilayda B. Onder, Mark Berlin
wiley +1 more source
Sensitivity analysis for generalized estimating equation with non‐ignorable missing data
Abstract Many incomplete‐data statistical inference procedures are developed under the missing at random (MAR) assumption. However, the MAR assumption has been criticized as being overly strong for real‐data problems, and is unverifiable by using observed data. To handle data that are missing not at random (MNAR), sensitivity analysis has been proposed
Hui Gong, Kin Wai Chan
wiley +1 more source

