Results 111 to 120 of about 30,695 (301)
Vine copula knockoffs for variable selection in gene expression studies
Canadian Journal of Statistics, EarlyView.Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina +3 more
wiley +1 more source
Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference
RisksThis study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions.
Marcos Escobar-Anel, Yiyao Jiao
doaj +1 more source
Newsvendor under Mean-Variance Ambiguity and Misspecification [PDF]
Consider a newsvendor problem with an unknown demand distribution. When addressing the issue of distributional uncertainty, we distinguish ambiguity under which the newsvendor does not differentiate demand distributions of common distributional ...
Chen, Zhi +3 more
core
Sparse maximum likelihood estimation of regression models
Canadian Journal of Statistics, EarlyView.Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley +1 more source
Evaluation of Simultaneous Equation Techniques in the Presence of Misspecification Error: A Monte Carlo Approach [PDF]
, 2014 One of the assumptions of Classical Linear Regression Model (CLRMA), is that the regression model be ‘correctly’ specified. If the model is not ‘correctly’ specified, the problem of model misspecification error arises.
ADEPOJU, ADEDAYO A., OJO, OLUWADARE O.
core +1 more source
Uncertainty quantification for misspecified machine learned interatomic potentials
npj Computational MaterialsThe use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which ...
Danny Perez +3 more
doaj +1 more source
Convex Models, MLS and Misspecification
The Annals of Statistics, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
How Robust is Robust Control in the Time Domain? [PDF]
By applying robust control the decision maker wants to make good decisions when his model is only a good approximation of the true one. Such decisions are said to be robust to model misspecification.
Marco P. Tucci
core
Order restricted row‐column cluster randomized design
Canadian Journal of Statistics, EarlyView.Abstract This research introduces a novel two‐stage cluster randomized design, the order restricted row‐column cluster randomized design. The new design builds upon the cluster randomized block design by incorporating a second layer of blocking, achieved through ranking cluster units that are randomly sampled from the population.
Dalton Hopper +2 more
wiley +1 more source
Mitigating measurement error in misspecified small area models
Canadian Journal of Statistics, EarlyView.Abstract In the framework of Small area estimation, we consider an area‐level model where a subset of covariates is measured with error. The extent of the error is assumed to be constant throughout the areas, and it is expressed by a scalar parameter γ$$ \gamma $$, which multiplies the deterministic covariance matrix of the estimator of the true ...
Diego Battagliese +3 more
wiley +1 more source