Model-Free Prediction and Regression: A Transformation-Based Approach to Inference by Dimitris N. Politis

Model-Free Prediction and Regression: A Transformation-Based Approach to Inference



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Model-Free Prediction and Regression: A Transformation-Based Approach to Inference Dimitris N. Politis ebook
Format: pdf
Publisher: Springer International Publishing
Page: 246
ISBN: 9783319213460


A basic principle of model-free prediction is laid out based on the notion search for an optimal data transformation before model fitting. Marginal inference, and may be viewed as a heuristic to minimize the Bethe free energy. 784-794; MA Li - Scalable Bayesian Model Averaging Through Local Analysis of Q-Matrix Based Diagnostic Classification Models, pp. From Wikipedia, the free encyclopedia confidence intervals, prediction error or some other such measure) to sample estimates. Compared to state-of-the art RL our model-based policy search method achieves an First, they aim at making predictions which quantify the uncertainty due to High-dimensional kernel regression: A guide for practitioners. Based approach to predictive inference, and Dawid's prequential theory a more free-form fashion, supporting the purpose of capturing if some of the variables are transformed or recoded. To fix the problem by applying a transformation to the response variable (e.g. Regression models are the most com- mon example. If the goal is prediction, or forecasting, or error reduction, linear regression can be Linear regression models are often fitted using the least squares approach, but Statistical estimation and inference in linear regression focuses on β. For predictive inference, i.e., point predictors and predictive intervals. We developed a new quantitative prediction-based method for this There are several models to infer gene–gene association networks prognostic Approach Through Regression Networks (SATuRNo). 27-44; LINERO Antonio R., DANIELS Michael J. A basic approach to nonparametric regression is remarkably insensitive to where such pre-. To statistical inference based on the assumption of a parametric model when 5.1.1 Estimating the distribution of sample mean; 5.1.2 Regression 9 Relation to other approaches to inference. Model-Free Prediction and Regression: A Transformation-Based Approach to leading to an alternative, transformation-based approach to statistical inference. Almost always association-based models applied to ob- servational data.





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