Results 91 to 100 of about 658,808 (305)

Robust MM-Estimation and Inference in Mixed Linear Models [PDF]

Mixed linear models are used to analyse data in many settings. These models generally rely on the normality assumption and are often fitted by means of the maximum likelihood estimator (MLE) or the restricted maximum likelihood estimator (REML). However,
Stephane Heritier, Samuel Copt
core  

Latent mixed models [PDF]

, 2009
The linear mixed model has been a major research interest of Dr Arthur Gilmour, motivated by problems arising in research data generated by agricultural scientists.
Thompson, R.
core  

Quasi-Monte Carlo estimation in generalized linear mixed models [PDF]

, 2006
Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, but parameter estimation is very challenging because the likelihood may involve high-dimensional integrals that are analytically intractable.
Pan, Jianxin, Thompson, Robin, Pan, J., Thompson, R.   +3 more
core   +1 more source

pH‐mediated activation of the lysosomal arginine sensor SLC38A9

FEBS Letters, EarlyView.
Cells monitor nutrient levels via the lysosomal transporter SLC38A9 to activate the mechanistic target of rapamycin complex 1 (mTORC1). This study reveals that SLC38A9 function is regulated by pH. We identified histidine 544 as a critical pH sensor that undergoes conformational changes to control amino acid efflux from lysosomes; therefore, it ...
Xuelang Mu, Ampon Sae Her, Tamir Gonen
wiley   +1 more source

Binary and Ordinal Random Effects Models Including Variable Selection [PDF]

, 2010
A likelihood-based boosting approach for fitting binary and ordinal mixed models is presented. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available.
Groll, Andreas, Tutz, Gerhard
core   +1 more source

Sobre la construcción del mejor predictor lineal insesgado (BLUP) y restricciones asociadas

Revista Colombiana de Estadística, 2007
A través del modelo lineal clásico de Gauss-Markov, se caracteriza el modelo de efectos mixtos, se aplica la técnica de multiplicadores de Lagrange para obtener los mejores predictores lineales (BLUP) y se ilustran los resultados de Searle (1997), donde ...
LUIS ALBERTO LÓPEZ, DIANA CAROLINA FRANCO, SANDRA PATRICIA BARRETO   +2 more
doaj  

Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)

Risks, 2016
Traditionally, actuaries have used run-off triangles to estimate reserve (“macro” models, on aggregated data). However, it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty
Arthur Charpentier, Mathieu Pigeon
doaj   +1 more source

Henderson's method approach to Kernel prediction in partially linear mixed models

Cumhuriyet Science Journal, 2020
In this article, we propose Kernel prediction in partially linear mixed models by using Henderson's method approach. We derive the Kernel estimator and the Kernel predictor via the mixed model equations (MMEs) of Henderson's that they give the best ...
Seçil Yalaz, Özge Kuran
doaj   +1 more source

Residual tail twisting in ascidian larvae is stabilized by asymmetric myofibrils that resist bilateral symmetry restoration

FEBS Letters, EarlyView.
Ascidian Ciona larvae initially show strong clockwise tail twisting, which is largely corrected during development. However, a small residual twist remains. This study shows that organized helical myofibrils in tail muscles mechanically stabilize this residual asymmetry, preventing complete restoration of bilateral symmetry and revealing how embryos ...
Yuki S. Kogure, Misaki K. Nishio, Satoru Okuda, Kohji Hotta   +3 more
wiley   +1 more source

Linear Mixed Models: Part I

, 2021
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as y = Xβ + 𝜖, where y is a vector of observations, X is a matrix of known covariates, β is a vector of unknown regression coefficients, and 𝜖 is a vector of (unobservable random ...
Jiming Jiang, Thuan Nguyen
openaire   +1 more source