Results 171 to 180 of about 2,844,453 (243)

A Rank‐Based CUSUM Monitoring Scheme for Time Between Events and Amplitude Data

open access: yesQuality and Reliability Engineering International, Volume 42, Issue 8, Page 4995-5014, December 2026.
ABSTRACT Monitoring time between events and amplitude (TBEA) data is critical in many applications, particularly in manufacturing, reliability engineering, and service operations. Yet, most existing TBEA control charts focus on a single quality characteristic and are typically developed under restrictive distributional assumptions.
Muhammad Arslan   +5 more
wiley   +1 more source

Nonparametric Conditional Copula Density Estimation With Applications to the Body Mass Index

open access: yesStat, Volume 15, Issue 4, December 2026.
ABSTRACT This study develops a nonparametric conditional copula density estimator that features a parametric carrier copula refined by an exponential series adjustment. We adopt the local likelihood approach for conditional distribution estimation and incorporate the influence of covariates via kernel smoothing.
Michael Z. Wu, Lan Zhou
wiley   +1 more source

Convergence in Distribution of the P–P Process in L1[0,1]$$ {\boldsymbol{L}}^{\mathbf{1}}\left[\mathbf{0},\mathbf{1}\right] $$

open access: yesStat, Volume 15, Issue 4, December 2026.
ABSTRACT We show that the percentile–percentile (P–P) process constructed from an independent and identically distributed sample of pairs converges in distribution in L1[0,1]$$ {L}^1\left[0,1\right] $$ if and only if the associated P–P curve is absolutely continuous.
Brendan K. Beare, Tetsuya Kaji
wiley   +1 more source

Enhancing symbolic image classification through Gaussian copulas and optimized distinguishing points. [PDF]

open access: yesPLoS One
Winarni S   +9 more
europepmc   +1 more source

Complex Versus Parsimonious Site‐Based Stochastic Ground Motion Models: Which One Is Better?

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 14, Page 3714-3731, November 2026.
ABSTRACT Stochastic ground motion models (GMMs) provide a probabilistic representation of seismic input and are increasingly important for uncertainty quantification (UQ) in earthquake engineering. This study focuses on site‐based stochastic GMMs, which learn the statistical features of selected datasets of seismic records and generate statistically ...
Maijia Su   +2 more
wiley   +1 more source

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