Results 51 to 60 of about 1,248,247 (319)
Targeted maximum likelihood estimation for a binary treatment: A tutorial
Statistics in Medicine, 2018 When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G‐formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are ...
M. Luque-Fernández +3 more
semanticscholar +1 more source
In situ molecular organization and heterogeneity of the Legionella Dot/Icm T4SS
FEBS Letters, EarlyView.We present a nearly complete in situ model of the Legionella Dot/Icm type IV secretion system, revealing its central secretion channel and identifying new components. Using cryo‐electron tomography with AI‐based modeling, our work highlights the structure, variability, and mechanism of this complex nanomachine, advancing understanding of bacterial ...
Przemysław Dutka +11 more
wiley +1 more source
Integration of audiovisual spatial signals is not consistent with maximum likelihood estimation
Cortex; a journal devoted to the study of the nervous system and behavior, 2019 Multisensory perception is regarded as one of the most prominent examples where human behaviour conforms to the computational principles of maximum likelihood estimation (MLE). In particular, observers are thought to integrate auditory and visual spatial
D. Meijer +3 more
semanticscholar +1 more source
Sequence determinants of RNA G‐quadruplex unfolding by Arg‐rich regions
FEBS Letters, EarlyView.We show that Arg‐rich peptides selectively unfold RNA G‐quadruplexes, but not RNA stem‐loops or DNA/RNA duplexes. This length‐dependent activity is inhibited by acidic residues and is conserved among SR and SR‐related proteins (SRSF1, SRSF3, SRSF9, U1‐70K, and U2AF1).
Naiduwadura Ivon Upekala De Silva +10 more
wiley +1 more source
Maximum likelihood estimation for Gaussian process with nonlinear drift
Nonlinear Analysis, 2018 We investigate the regression model Xt = θG(t) + Bt, where θ is an unknown parameter, G is a known nonrandom function, and B is a centered Gaussian process.
Yuliya Mishura +2 more
doaj +1 more source
Maximum likelihood estimation in log-linear models
, 2012 We study maximum likelihood estimation in log-linear models under conditional Poisson sampling schemes. We derive necessary and sufficient conditions for existence of the maximum likelihood estimator (MLE) of the model parameters and investigate ...
Fienberg, Stephen E. +1 more
core +3 more sources
Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy
Mathematical Problems in Engineering, 2019 The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult.
Fan Yang, Hu Ren, Zhili Hu
semanticscholar +1 more source
FEBS Letters, EarlyView.The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source
Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 more
doaj +1 more source
MaxEnt assisted MaxLik tomography
, 2004 Maximum likelihood estimation is a valuable tool often applied to inverse problems in quantum theory. Estimation from small data sets can, however, have non unique solutions.
Hradil, Z., Rehacek, J.
core +1 more source