Results 31 to 40 of about 8,657 (149)
Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
Journal of Economic Surveys, EarlyView.ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
wiley +1 more source
Monitoring panels of sparse functional data
Journal of Time Series Analysis, EarlyView.Panels of random functions are common in applications of functional data analysis. They often occur when sequences of functions are observed at a number of different locations. We propose a methodology to monitor for structural breaks in such panels and to identify the changing components with statistical certainty.
Tim Kutta +2 more
wiley +1 more source
A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population [PDF]
, 2014 In survey analysis, the estimation of the cumulative distribution function (cdf) is of great interest: it allows for instance to derive quantiles estimators or other non linear parameters derived from the cdf.
Casanova, Sandrine, Leconte, Eve
core +5 more sources
Testing Mean Stability of Heteroskedastic Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT Time series models are often fitted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fitting models with spurious dynamics that lead to unsupported and controversial conclusions ...
Violetta Dalla +2 more
wiley +1 more source
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
, 2019 We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much richer patterns of heterogeneity in the selection process and effect of the ...
Chernozhukov, Victor +2 more
core +1 more source
Kyklos, EarlyView.ABSTRACT This study examines academic dishonesty among university students, focusing on peer influence, detection risk, effort, and sanctions in proctored online and offline exams. Drawing on 259 survey responses collected from German universities after the COVID‐19‐driven transition to online formats, it applies a utility‐based framework, combined ...
Thomas Ehrmann +2 more
wiley +1 more source
Estimating a Signal In the Presence of an Unknown Background
, 2012 We describe a method for fitting distributions to data which only requires knowledge of the parametric form of either the signal or the background but not both. The unknown distribution is fit using a non-parametric kernel density estimator.
López, Angel M., Rolke, Wolfgang A.
core +1 more source
Risks of ignoring uncertainty propagation in AI‐augmented security pipelines
Risk Analysis, EarlyView.Abstract The use of AI technologies is being integrated into the secure development of software‐based systems, with an increasing trend of composing AI‐based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and seriously threatens safety‐critical domains.
Emanuele Mezzi +3 more
wiley +1 more source
Kernel Estimation for Panel Data with Heterogeneous Dynamics
, 2018 This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel
Okui, Ryo, Yanagi, Takahide
core +1 more source
Risk Analysis, EarlyView.ABSTRACT In absence of sufficient data, structured expert judgment is a suitable method to estimate uncertain quantities. While such methods are well established for individual variables, eliciting their dependence in a structured manner is a less explored field of research.
Guus Rongen +3 more
wiley +1 more source