Results 61 to 70 of about 33,702 (146)
Likelihood Estimation for Stochastic Differential Equations with Mixed Effects
Scandinavian Journal of Statistics, EarlyView.ABSTRACT Stochastic differential equations provide a powerful tool for modelling dynamic phenomena affected by random noise. When time series are observed for several experimental units, it is often the case that some of the parameters vary between the individual experimental units.
Fernando Baltazar‐Larios +2 more
wiley +1 more source
Causal Effect Estimation With TMLE: Handling Missing Data and Near Violations of Positivity
Biometrical Journal, Volume 68, Issue 3, June 2026.ABSTRACT We evaluate the performance of targeted maximum likelihood estimation (TMLE) for estimating the average treatment effect in missing data scenarios under varying levels of positivity violations. We employ model‐ and design‐based simulations, with the latter using undersmoothed highly adaptive lasso on the “WASH Benefits Bangladesh” data set to ...
Christoph Wiederkehr +2 more
wiley +1 more source
Subgroup Identification via Multiple Change Point Detection: Methods and Applications
WIREs Computational Statistics, Volume 18, Issue 2, June 2026.Subgroup identification methods facilitate the discovery of clinically meaningful subpopulations with differing disease progression, improving personalized risk assessment and treatment strategies. ABSTRACT Subgroup identification is a significant research area in statistics and machine learning, aiming to partition a heterogeneous population into more
Yaguang Li +3 more
wiley +1 more source
On the Foundational Arguments of Sufficient Dimension Reduction
WIREs Computational Statistics, Volume 18, Issue 2, June 2026.Contemporary Sufficient Dimension Reduction, a versatile method for extracting material information from data, can serve as a preprocessor for classical modeling and inference, or as a standalone theory that leads directly to statistical inference. ABSTRACT Sufficient dimension reduction (SDR) refers to supervised methods of dimension reduction that ...
R. Dennis Cook
wiley +1 more source
Educational Measurement: Issues and Practice, Volume 45, Issue 2, Summer 2026.Abstract Diagnostic classification models (DCMs) assess students’ mastery of cognitive attributes to provide personalized ability profiles. Retrofitting DCMs to large‐scale mathematics assessments usually relies on inferred Q‐matrices, which can reduce accuracy and diagnostic value.
Farshad Effatpanah +4 more
wiley +1 more source
The Role of Price‐Volatility Cojumps in Volatility Forecasting
Journal of Futures Markets, Volume 46, Issue 5, Page 931-951, May 2026.ABSTRACT This paper investigates whether simultaneous jumps in prices and volatility improve volatility forecasting. Using up‐to‐date high‐frequency S&P 500 and VIX data, we identify price‐volatility cojumps at the intraday granularity and construct upside, downside, and asymmetric measures.
Kefu Liao
wiley +1 more source
Statistics in Medicine, Volume 45, Issue 10-12, May 2026.ABSTRACT Real‐world randomized controlled trials (RCTs) evaluating multifaceted interventions often employ multiple study outcomes to measure treatment effects on a small set of underlying constructs. Motivated by a longitudinal RCT evaluating a behavioural intervention, the Arthritis Health Journal (AHJ), we propose a latent‐factor multivariate ...
Lulu Guo +3 more
wiley +1 more source
Statistics in Medicine, Volume 45, Issue 10-12, May 2026.ABSTRACT For composite time‐to‐event outcomes, the win ratio as a relative measure ignores ties resulting from non‐occurrence of events, which can obscure important context in regression settings where event rates—and hence the proportion of ties—vary over time and across covariate values.
Lu Mao
wiley +1 more source
Hidden Markov Models for Bounded, Inflated Time Series: Forecasting Icing on Wind Turbine Blades
Wind Energy, Volume 29, Issue 5, May 2026.ABSTRACT Time series analysis of icing‐induced power loss in wind turbines pose several challenges: the response is bounded, serially dependent, intermittently missing, highly dispersed, and often inflated at a single value. We address these challenges with discrete‐time hidden Markov models for a discrete‐continuous process assumed to follow a mixture
Albert S. Bisgaard +3 more
wiley +1 more source
How do ecologists estimate occupancy in practice?
Ecography, Volume 2026, Issue 5, May 2026.Over 20 years ago, ecologists were introduced to the site occupancy model (SOM) for estimating occupancy rates from detection‐nondetection data. In the ensuing decades, the SOM and its hierarchical modeling extensions have become mainstays of quantitative ecology, and estimating occupancy rates has become one of the most common applications of ...
Benjamin R. Goldstein +9 more
wiley +1 more source