Results 51 to 60 of about 257,916 (299)

Extending the scope of empirical likelihood

The Annals of Statistics, 2009
This article extends the scope of empirical likelihood methodology in three directions: to allow for plug-in estimates of nuisance parameters in estimating equations, slower than $\sqrt{n}$-rates of convergence, and settings in which there are a relatively large number of estimating equations compared to the sample size.
Hjort, Nils Lid, McKeague, Ian W., van Keilegom, Ingrid   +2 more
openaire   +7 more sources

Cell geometry and membrane protein crowding constrain Escherichia coli growth rate, overflow metabolism, respiration, and maintenance energy

FEBS Letters, EarlyView.
The physical dimensions and shape of bacterial cells define the surface area available to acquire nutrients and the volume available for synthesizing proteins and DNA. Here, we use computational systems biology to decode the importance of cell geometry as a major determinant of prokaryotic phenotype, including growth rate and metabolic efficiency. This
Ross P. Carlson, Tomáš Gedeon, Mauricio Garcia Benitez, Campbell Putnam, William R. Harcombe, Radhakrishnan Mahadevan, Ashley E. Beck   +6 more
wiley   +1 more source

Empirical likelihood confidence intervals for complex sampling designs

, 2016
We define an empirical likelihood approach which gives consistent design-based confidence intervals which can be calculated without the need of variance estimates, design effects, resampling, joint inclusion probabilities and linearization, even when the
De La Riva Torres, O., Berger, Y.G.
core   +1 more source

Pre‐analytical optimization of cell‐free DNA and extracellular vesicle‐derived DNA for mutation detection in liquid biopsies

Molecular Oncology, EarlyView.
Pre‐analytical handling critically determines liquid biopsy performance. This study defines practical best‐practice conditions for cell‐free DNA (cfDNA) and extracellular vesicle–derived DNA (evDNA), showing how processing time, storage conditions, tube type, and plasma input volume affect DNA integrity and mutation detection.
Jonas Dohmen, Lucrezia De Santis, Bret M. Stephens, Vincent Bernard, Jörg C. Kalff, Lara Braun, Phillipp Leyendecker, Johannes Röttgen, Daniel Weissinger, Anirban Maitra, Paola A. Guerrero, Alexander Semaan   +11 more
wiley   +1 more source

melt: Multiple Empirical Likelihood Tests in R

Journal of Statistical Software
Empirical likelihood enables a nonparametric, likelihood-driven style of inference without relying on assumptions frequently made in parametric models.
Eunseop Kim, Steven N. MacEachern, Mario Peruggia   +2 more
doaj   +1 more source

Empirical likelihood inference for the Rao-Hartley-Cochran sampling design

, 2016
The Hartley-Rao-Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley-Rao-Cochran sampling design.
Yves G. Berger, Berger, Yves G.
core   +1 more source

Deciphering transcriptional plasticity in pancreatic ductal adenocarcinoma reveals alterations in sensory neuron innervation

Molecular Oncology, EarlyView.
Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova, Michele Montrone, Uday Rangaswamy, Francesco Diversi, Irene Schiavo, Denise Ferrarini, Roberta Di Florio, Irene Longo, Michele Coscia, Nicola Zamboni, Giorgia Demontis, Lisa Veghini, Vincenzo Corbo, Remo Sanges, Paul Heppenstall   +14 more
wiley   +1 more source

Restricted Empirical Likelihood Estimation for Time Series Autoregressive Models

Journal of Statistical Theory and Applications (JSTA), 2021
In this paper, we first illustrate the restricted empirical likelihood function, as an alternative to the usual empirical likelihood. Then, we use this quasi-empirical likelihood function as a basis for Bayesian analysis of AR(r) time series models.
Mahdieh Bayati, S.K. Ghoreishi, Jingjing Wu   +2 more
doaj   +1 more source

Empirical likelihood estimation of the spatial quantile regression [PDF]

, 2012
The spatial quantile regression model is a useful and flexible model for analysis of empirical problems with spatial dimension. This paper introduces an alternative estimator for this model.
Kostov, Phillip
core   +1 more source

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source