Results 51 to 60 of about 2,756 (146)

Association Between Abnormal DNA Methylation and Altered Transcriptome in Muscle Five Years After Critical Illness

open access: yesJournal of Cachexia, Sarcopenia and Muscle, Volume 17, Issue 1, February 2026.
ABSTRACT Background Critically ill patients requiring intensive care unit (ICU) admission suffer from muscle weakness that persists for years. Recently, altered RNA expression was documented in muscle of former ICU patients 5 years after critical illness that suggested disrupted mitochondrial function, disturbed lipid metabolism and fibrosis, of which ...
Ceren Uzun Ayar   +4 more
wiley   +1 more source

Stabilizing Inference in Dirichlet Regression via Ridge‐Penalized Model

open access: yesStatistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.
ABSTRACT We propose a penalized Dirichlet regression framework for modeling compositional data, using a softmax link to ensure that the mean vector lies on the simplex and to avoid log‐ratio transformations or zero replacement. The model is formulated in a GLM‐like setting and incorporates an ℓ2$$ {\mathrm{\ell}}_2 $$ (ridge) penalty on the regression ...
Andrea Nigri
wiley   +1 more source

Joint analysis of dispersed count‐time data using a bivariate latent factor model

open access: yesBritish Journal of Mathematical and Statistical Psychology, Volume 79, Issue 1, Page 207-228, February 2026.
Abstract In this study, we explore parameter estimation for a joint count‐time data model with a two‐factor latent trait structure, representing accuracy and speed. Each count‐time variable pair corresponds to a specific item on a measurement instrument, where each item consists of a fixed number of tasks.
Cornelis J. Potgieter   +3 more
wiley   +1 more source

Endogeneity Corrections in Binary Outcome Models With Nonlinear Transformations: Identification and Inference

open access: yesOxford Bulletin of Economics and Statistics, Volume 88, Issue 1, Page 45-56, February 2026.
ABSTRACT For binary outcome models, an endogeneity correction based on nonlinear rank‐based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: Either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the ...
Alexander Mayer, Dominik Wied
wiley   +1 more source

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