[go: up one dir, main page]

Burr et al., 2005 - Google Patents

Biased regression: The case for cautious application

Burr et al., 2005

Document ID
10965909822959321272
Author
Burr T
Fry H
Publication year
Publication venue
Technometrics

External Links

Snippet

Biased regression (BR) methods have received only lukewarm approval, largely because of the practical difficulty in using data-driven methods to find a biased estimator that is better than ordinary least squares (OLS). Nevertheless, we believe there are situations where BR …
Continue reading at www.tandfonline.com (other versions)

Similar Documents

Publication Publication Date Title
Khatun Applications of normality test in statistical analysis
Giacomini et al. Robust Bayesian inference for set‐identified models
Adams et al. Multivariate phylogenetic comparative methods: evaluations, comparisons, and recommendations
Fortmann-Roe Consistent and clear reporting of results from diverse modeling techniques: the A3 method
Javanmard et al. False discovery rate control via debiased lasso
Turhan et al. Analysis of Naive Bayes’ assumptions on software fault data: An empirical study
Warming-Rasmussen et al. Quality dimensions in external audit services-An external user perspective
Decarlo Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models
Tendick et al. Distance-based resource quantification for sets of quantum measurements
Bürkner et al. Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Wagner et al. Finite-sample effects and resampling plans: applications to linear classifiers in computer-aided diagnosis
Burr et al. Biased regression: The case for cautious application
Pallis et al. Validation and interpretation of Web users’ sessions clusters
Li et al. Multi-dimensional domain generalization with low-rank structures
Duan et al. Distance guided classification with gene expression programming
Henrard Calibration in finance: Very fast greeks through algorithmic differentiation and implicit function
Bertoli et al. Bayesian approach for the zero-modified Poisson–Lindley regression model
Todo et al. Fitting unstructured finite mixture models in longitudinal design: a recommendation for model selection and estimation of the number of classes
Parmet et al. Factor analysis revisited–How many factors are there?
CN114610274A (en) Improved demand priority evaluation method based on analytic hierarchy process
Binkytė-Sadauskienė et al. Causal discovery for fairness
You A robust queueing network analyzer based on indices of dispersion
Clarke et al. Prediction in several conventional contexts
Rabitti Computer Experiments: Sensitivity Analysis and Interactions
Conde-Amboage et al. Application of quantile regression models for biomedical data