Results 11 to 20 of about 7,218 (105)
Leveraging Rank Information for Robust Regression Analysis: A Nomination Sampling Approach. [PDF]
Stat MedABSTRACT This paper introduces a novel methodology for robust regression analysis when traditional mean regression falls short due to the presence of outliers. Unlike conventional approaches that rely on simple random sampling (SRS), our methodology leverages median nomination sampling (MedNS) by utilizing readily available ranking information to ...
Loewen N, Jafari Jozani M.
europepmc +2 more sources
Nonclassical Measurement Error in Farmland Markets with Implications for Ricardian Analysis
American Journal of Agricultural Economics, EarlyView.Abstract Nonclassical measurement error threatens the validity of empirical economic models. We examine the extent to which land value measures that are commonly used in studies of the US farmland market are subject to nonclassical measurement error. We consider differences in county‐level land values from two popular data sources: (1) self‐reported ...
Daniel P. Bigelow, Margaret Jodlowski
wiley +1 more source
Alternative Approaches for Estimating Highest‐Density Regions
International Statistical Review, EarlyView.Summary Among the variety of statistical intervals, highest‐density regions (HDRs) stand out for their ability to effectively summarise a distribution or sample, unveiling its distinctive and salient features. An HDR represents the minimum size set that satisfies a certain probability coverage, and current methods for their computation require ...
Nina Deliu, Brunero Liseo
wiley +1 more source
Journal of Anatomy, EarlyView.Animals were sensitized with mBSA emulsified in CFA/IFA and subsequently received three intra‐articular injections of mBSA into either the TMJ or the knee (once per week). Mechanical hyperalgesia was assessed using electronic von Frey testing after arthritis establishment at 24 h or 7 days following the third intra‐articular injection.
Ana Carolina de Figueiredo Costa +2 more
wiley +1 more source
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
Forecasting Local Ionospheric Parameters Using Transformers
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.Abstract We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location.
D. J. Alford‐Lago +4 more
wiley +1 more source
Kyklos, Volume 79, Issue 1, Page 112-128, February 2026.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
Environmetrics, Volume 37, Issue 1, January 2026.ABSTRACT Compound events (CEs), commonly defined as the “combination of multiple drivers and/or hazards that contributes to societal or environmental risk”, often result in amplified impacts compared to individual hazards. In order to estimate the return period of bivariate CEs, a novel nonparametric approach employing bivariate Generalized Pareto ...
Grégoire Jacquemin +3 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