Multivariate analysis and fuzziness

Results 101 to 110 of about 9,592 (232)

Assessing the Number of Components in Mixture Models: a Review. [PDF]

Despite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue.
Ana Oliveira-Brochado, Francisco Vitorino Martins +1 more
core

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
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

Near‐Infrared Spectroscopy and Chemometrics as a Tool for Nondestructive Polymer Degradation Analysis in Photovoltaic Modules

Solar RRL, Volume 10, Issue 6, 29 March 2026.
The long‐term reliability of photovoltaic (PV) modules is crucial, and polymeric components play an important role in ensuring it. This makes nondestructive methods for detecting material degradation valuable and essential. This study proposes and validates a methodology that utilizes near‐infrared (NIR) spectroscopy and chemometric techniques to ...
Anika Gassner +3 more
wiley +1 more source

EOF‐Based Bias Correction of Near‐Surface Wind Speed Over China Reveals Stronger Future Trends and Variability

Geophysical Research Letters, Volume 53, Issue 6, 28 March 2026.
Abstract As near‐surface wind speed (NSWS) largely controls wind power generation, robust projection is vital to wind energy planning and broader sustainability goals. However, the predictive skill of climate models for NSWS remains uncertain. Analysis of Coupled Model Intercomparison Project Phase 6 simulations shows that the models reproduce the mean
Yang Xu +4 more
wiley +1 more source

Adaptive Clustering and Feature Selection for Categorical Time Series Using Interpretable Frequency-Domain Features. [PDF]