Results 81 to 90 of about 724,462 (280)

Alternative implementations of Monte Carlo EM algorithms for likelihood inferences

Genetics Selection Evolution, 2001
Two methods of computing Monte Carlo estimators of variance components using restricted maximum likelihood via the expectation-maximisation algorithm are reviewed.
Sorensen Daniel, García-Cortés Louis
doaj   +1 more source

Genetic analysis of growth curve in Moghani Sheep using Bayesian and restricted maximum likelihood. [PDF]

J Anim Sci, 2023
Rashedi Dehsahraei A, Ghaderi-Zefrehei M, Rafeie F, Zakizadeh S, Shirani Shamsabadi J, Elahi Torshizi M, Neysi S, Rahmatalla SA.   +7 more
europepmc   +1 more source

CHARACTERIZING UNCERTAIN OUTCOMES WITH THE RESTRICTED HT TRANSFORMATION [PDF]

Restrictions on the hyperbolic trigonometric (HT) transformation are imposed to guarantee that a probability density function is obtained from the maximum likelihood estimation.
Moffitt, L. Joe
core   +1 more source

Positive estimation of the between-group variance component in one-way anova and meta-analysis [PDF]

, 2000
Positive estimators of the between-group (between-study) variance are proposed. Explicit variance formulae for the estimators are given and approximate confidence intervals for the between-group variance are constructed, as our proposal to a long ...
Hartung, Joachim, Makambi, Kepher H.
core   +1 more source

Longitudinal circulating tumor DNA profiling in patients with advanced endometrial cancer using an off‐the‐shelf targeted NGS panel

Molecular Oncology, EarlyView.
Intratumour heterogeneity complicates precision management of advanced endometrial cancer. Circulating tumor DNA (ctDNA) offers a minimally invasive strategy to capture tumor evolution and therapeutic resistance. Here, we compare tumor‐agnostic NGS with tumor‐informed ddPCR, outlining their relative sensitivity, concordance, and clinical implications ...
Carlos Casas‐Arozamena, Karin Teien Lande, Eva Diaz, Ana Vilar, Juan Cueva, Efigenia Arias, Victoria Sampayo, Alicia Abalo, Nerea González, Eva Colás, Antonio Gil‐Moreno, Miguel Abal, Gema Moreno‐Bueno, Therese Sørlie, Kristina Lindemann, Laura Muinelo‐Romay   +15 more
wiley   +1 more source

The Genetic Parameters for Milk, Fat and Protein Yields in Holstein Cows

Scientific Papers Animal Science and Biotechnologies, 2023
The aim of this study was to analyse methodology of genetic parameters estimation and breeding value prediction for milk, fat and protein yield in the first lactation of Holstein cattle using Best Linear Unbiased Prediction procedure- Restricted Maximum ...
Rodica Stefania Pelmus, Horia Grosu, Mircea Cătălin Rotar, Mihail Alexandru Gras, Cristina Lazăr, Florin Popa   +5 more
doaj  

Testing When a Parameter Is on the Boundary of the Maintained Hypothesis [PDF]

This paper considers testing problems where several of the standard regularity conditions fail to hold. We consider the case where (i) parameter vectors in the null hypothesis may lie on the boundary of the maintained hypothesis and (ii) there may be a ...
Donald W.K. Andrews
core  

Differential regulation of ZFAS1 splice variants by endoplasmic reticulum stress in hepatocyte cell lines

FEBS Open Bio, EarlyView.
ZFAS1 is a lncRNA promoting cell proliferation and migration, exhibiting high expression in various cancers. It is conserved, widely expressed, and produces multiple splice variants with unclear roles. We identified several splice variants in hepatocyte models, and found that inhibiting or suppressing regulators of the unfolded protein response (PERK ...
Sébastien Soubeyrand, Paulina Lau, Ruth McPherson   +2 more
wiley   +1 more source

MIXED-EFFECT MODELS WITH RESTRICTED MAXIMUM LIKELIHOOD (REML), BOOT-STRAPPED REML AND BAYESIAN INFERENCE IN APPLICATION OF GAPMINDER DATA

Barekeng
Mixed effects model combines fixed effects and random effects, allowing for the analysis of data with both fixed and random variations. This modeling approach is widely utilized across various fields.
Asysta Amalia Pasaribu, Kusman Sadik, Anang Kurnia   +2 more
doaj   +1 more source

Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models [PDF]

We discuss computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples.
Jurgen A. Doornik, Marius Ooms
core