Results 141 to 150 of about 1,312,626 (282)
Robust Lasso Regression Using Tukey's Biweight Criterion
, 2018 The adaptive lasso is a method for performing simultaneous parameter estimation and variable selection. The adaptive weights used in its penalty term mean that the adaptive lasso achieves the oracle property.
Alan Welsh (243951) +2 more
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Arthro‐Cross Compression Technique for Anterior Cruciate Ligament Avulsion Fracture Fixation
Arthroscopy Techniques, EarlyView.Abstract Isolated anterior cruciate ligament avulsion fractures of the tibial eminence are uncommon in orthopaedic practice. These injuries were traditionally treated with open reduction and internal fixation, but arthrotomy‐related morbidity has led to an increased use of arthroscopic techniques.
Krishan Kumar +4 more
wiley +1 more source
BMC Medical Research MethodologyBackground In the competing risk settings, the association of predictor variables with different event types is a crucial task. Accurate variable selection resolves the issue of confounding in research on causes and enables unbiased estimation of ...
Fatemeh Javanmardi +3 more
doaj +1 more source
Data-Adaptive Multivariate Test for Genomic Studies Using Fused Lasso
MathematicsIn genomic studies, univariate analysis is commonly used to discover susceptible variants. It applies univariate regression for each variant and tests the significance of the regression coefficient or slope parameter.
Masao Ueki
doaj +1 more source
Adaptive LASSO quantile regression with fixed effects
Applied Mathematical ModellingQuantile regression with fixed effects for longitudinal data accounts for individual-specific intercepts.When the number of individuals is large relative to the number of repeated measurements and the covariate dimension is high, we propose a dimension-reduction approach based on the least absolute shrinkage and selection operator (LASSO) penalty ...
Danilevicz, Ian Meneghel +2 more
openaire +2 more sources
Adaptive Lasso for sparse highdimensional regression
, 2006 Summary. We study the asymptotic properties of adaptive LASSO estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase with the sample size. We consider variable selection using the adaptive LASSO, where
Shuangge Ma, Cun-hui Zhang, Jian Huang
core
Model Selection via Standard Error Adjusted Adaptive Lasso [PDF]
, 2010 1 online resource (PDF, 25 pages)Qian, Wei; Yang, Yuhong. (2010). Model Selection via Standard Error Adjusted Adaptive Lasso.
Yang, Yuhong, Qian, Wei
core
Business Strategy and the Environment, EarlyView.ABSTRACT In a business environment characterized by technological turbulence and an increasing societal demand for greater sustainability, companies seeking to enhance their growth are compelled to foster organizational cultures that promote continuous innovation.
José Bocoya‐Maline +2 more
wiley +1 more source
The variable selection of two-part regression model for semicontinuous data.
PLoS ONEIn many research fields, measurement data containing too many zeros are often called semicontinuous data. For semicontinuous data, the most common method is the two-part model, which establishes the corresponding regression model for both the zero-valued
Yahui Lu, Aiyi Liu, Tao Jiang
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Business Strategy and the Environment, EarlyView.ABSTRACT Transition intermediaries play a pivotal role in configuring networks aligning the interests of stakeholders to achieve sustainable goals. However, the transition literature has paid little attention to the influence intermediaries can have in strengthening organisational capabilities. Especially in sectors that are slow to change, such as the
Horacio Gonzalez +2 more
wiley +1 more source