The following pages link to To explain or to predict? (Q906529):
Displaying 26 items.
- A Survey of Differentially Private Regression for Clinical and Epidemiological Research (Q6088255) (← links)
- On the exploration of regression dependence structures in multidimensional contingency tables with ordinal response variables (Q6097552) (← links)
- An evolutionary estimation procedure for generalized semilinear regression trees (Q6148396) (← links)
- SUBiNN: a stacked uni- and bivariate \(k\)NN sparse ensemble (Q6161659) (← links)
- Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy (Q6185714) (← links)
- Explainable ensemble trees (Q6538404) (← links)
- Flexible model-based non-negative matrix factorization with application to mutational signatures (Q6544880) (← links)
- Using cross-validation methods to select time series models: promises and pitfalls (Q6559931) (← links)
- Forbidden Knowledge and Specialized Training: A Versatile Solution for the Two Main Sources of Overfitting in Linear Regression (Q6562770) (← links)
- Estimating retail demand with Poisson mixtures and out-of-sample likelihood (Q6571852) (← links)
- Explainable AI for operational research: a defining framework, methods, applications, and a research agenda (Q6572853) (← links)
- A special issue on: Actual impact and future perspectives on stochastic modelling in business and industry (Q6574672) (← links)
- A zero-inflated endemic-epidemic model with an application to measles time series in Germany (Q6595078) (← links)
- Information criteria for model selection (Q6602021) (← links)
- Differential equations in data analysis (Q6602133) (← links)
- Statistical plasmode simulations-potentials, challenges and recommendations (Q6618472) (← links)
- A comparison of full model specification and backward elimination of potential confounders when estimating marginal and conditional causal effects on binary outcomes from observational data (Q6625330) (← links)
- A neutral comparison of algorithms to minimize \(L_0\) penalties for high-dimensional variable selection (Q6625366) (← links)
- Predicting class switch recombination in B-cells from antibody repertoire data (Q6625478) (← links)
- Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population (Q6626877) (← links)
- Confidence, prediction, and tolerance in linear mixed models (Q6627212) (← links)
- Selection of variables for multivariable models: opportunities and limitations in quantifying model stability by resampling (Q6627893) (← links)
- Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection (Q6629353) (← links)
- Bayesian approaches to variable selection: a comparative study from practical perspectives (Q6637079) (← links)
- Post-estimation shrinkage in full and selected linear regression models in low-dimensional data revisited (Q6649361) (← links)
- The InterModel Vigorish as a Lens for understanding (and quantifying) the value of item response models for dichotomously coded items (Q6657623) (← links)
