Results 71 to 80 of about 1,208,864 (273)

La norma L1 como alternativa a la norma L2 en el ajuste de la regresión

open access: yesRect@, 2002
En este trabajo analizamos el desarrollo y los conceptos de las normas L1 y L2 y se comparan con algunos ejemplos. Por una parte la norma L1 es óptima bajo los supuestos de que los errores tienen la distribución de Laplace.
Carlos N. Bouza, Luis C. Martínez
doaj  

Crucial parameters for precise copy number variation detection in formalin‐fixed paraffin‐embedded solid cancer samples

open access: yesMolecular Oncology, EarlyView.
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris   +10 more
wiley   +1 more source

Circular RNA expression landscapes in myelodysplastic neoplasms: Associations with mutational signatures and disease progression

open access: yesMolecular Oncology, EarlyView.
In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge   +17 more
wiley   +1 more source

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

open access: yesMolecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Nonparametric Functional Least Absolute Relative Error Regression: Application to Econophysics

open access: yesMathematics
In this paper, we propose an alternative kernel estimator for the regression operator of scalar response variable S given a functional random variable T that takes values in a semi-metric space.
Ali Laksaci   +2 more
doaj   +1 more source

On the Consistency of Ordinal Regression Methods [PDF]

open access: yes, 2017
Many of the ordinal regression models that have been proposed in the literature can be seen as methods that minimize a convex surrogate of the zero-one, absolute, or squared loss functions. A key property that allows to study the statistical implications
Bach, Francis   +2 more
core   +2 more sources

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

open access: yesMolecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel   +6 more
wiley   +1 more source

Least absolute deviation estimation of linear econometric models: A literature review [PDF]

open access: yes
Econometricians generally take for granted that the error terms in the econometric models are generated by distributions having a finite variance. However, since the time of Pareto the existence of error distributions with infinite variance is known ...
Dasgupta, Madhuchhanda, Mishra, SK
core   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

open access: yesMolecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova   +25 more
wiley   +1 more source

Regularization Methods Based on the Lq-Likelihood for Linear Models with Heavy-Tailed Errors

open access: yesEntropy, 2020
We propose regularization methods for linear models based on the Lq-likelihood, which is a generalization of the log-likelihood using a power function. Regularization methods are popular for the estimation in the normal linear model.
Yoshihiro Hirose
doaj   +1 more source

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