Results 61 to 70 of about 104,768 (268)

METHODS OF ESTIMATION IN MULTIPLE LINEAR REGRESSION: APPLICATION TO CLINICAL DATA MÉTODOS DE ESTIMACIÓN EN REGRESIÓN LINEAL MÚLTIPLE: APLICACIÓN A DATOS CLÍNICOS MÉTODOS DE ESTIMAÇÃO EM REGRESSÃO LINEAR MÚLTIPLA: APLICAÇÃO A DADOS CLÍNICOS

Revista Colombiana de Estadística, 2008
In this paper, we show different parameters estimation forms for multiple linear regression model. We used clinical data, where the interest was to verify the relationship among the mechanical assay maximum stress with femoral mass, femoral diameter and ...
Coelho-Barros Emílio Augusto, Simões Priscila Angelotti, Achcar Jorge Alberto, Martinez Edson Zangiacomi, Shimano Antônio Carlos   +4 more
doaj  

Developing a simple artificial intelligence fuzzy-based model for estimating saturated hydraulic conductivity of soil

Scientific Reports
Saturated hydraulic conductivity is one of the important physical properties of soil in modeling water and solute transport, irrigation management, and drainage issues. Laboratory and field methods for directly measuring this parameter are time-consuming
Mohammad Naderianfar
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Automating approximate Bayesian computation by local linear regression

BMC Genetics, 2009
Background In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular.
Thornton Kevin R
doaj   +1 more source

Finite-Sample Inference for Sparsely Permuted Linear Regression

CoRR
We study a linear observation model with an unknown permutation called \textit{permuted/shuffled linear regression}, where responses and covariates are mismatched and the permutation forms a discrete, factorial-size parameter. The permutation is a key component of the data-generating process, yet its statistical investigation remains challenging due to
Hirofumi Ota, Masaaki Imaizumi
openaire   +2 more sources

Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer

Molecular Oncology, EarlyView.
Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas, Barbara Karakyriakou, Patricia C. Muskus, Alyssa M. Roberts, Justin Rendleman, Alexa Warren, Hanxu Lu, Kimberly A. Birmingham, Malone K. Friedman, Abigail E. Goen, Huijuan Yang, Owen M. Wilkins, Fred W. Kolling, Eugene Demidenko, Julie M. Jorns, Brock C. Christensen, Todd W. Miller   +16 more
wiley   +1 more source

truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

Journal of Statistical Software, 2014
Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use ...
Maria Karlsson, Anita Lindmark
doaj   +1 more source

On Inference of the Linear Regression Model with Groupwise Heteroscedasticity

Pakistan Journal of Statistics and Operation Research, 2017
The performance of heteroscedasticity consistent covariance matrix estimators (HCCMEs), namely, HC0, HC1, HC2, HC3 and HC4 have been evaluated by numerous researchers for the heteroscedastic linear regression models. This study focuses on examining the performance of these covariance estimators in case of groupwise heteroscedasticity.
Abdul Majid, Muhammad Aslam, Muhammad Amanullah, Saima Altaf   +3 more
openaire   +2 more sources

Molecular dynamics simulations of positively selected codons in FcγRI reveal novel biochemical binding properties

FEBS Open Bio, EarlyView.
Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young, Omar Arias‐Gaguancela, Fiona Gibson, Morgan Grainger, Jodie Score, Amit Nathubhai, Mauricio Cafiero, Lee Richard Machado   +7 more
wiley   +1 more source

An Rkhs Approach for Pivotal Inference in Functional Linear Regression

Statistica Sinica
We develop methodology for testing hypotheses regarding the slope function in functional linear regression for time series via a reproducing kernel Hilbert space approach. In contrast to most of the literature, which considers tests for the exact nullity of the slope function, we are interested in the null hypothesis that the slope function vanishes ...
Dette, Holger, Tang, Jiajun
openaire   +2 more sources