Model-Free Prediction and Regression: A Transformation-Based Approach to Inference (Frontiers in Probability and the Statistical Sciences)
Model-Free Prediction and Regression explores a transformation-based approach to inference, bypassing traditional model assumptions in statistical analysis. This work in the Frontiers in Probability and the Statistical Sciences series offers robust methods for prediction and regression, enhancing flexibility and reliability in probabilistic applications for statisticians and researchers.
About This Book
This volume presents a transformation-based approach to model-free prediction and regression, focusing on inference within the frontiers of probability and statistical sciences.
The methodology emphasizes techniques that enable reliable predictions and regressions without assuming specific underlying models, providing flexibility in statistical applications.
Authored by Dimitris N. Politis, the book contributes to the ongoing development of nonparametric and semiparametric methods in probability and statistics.
It serves as a resource for researchers and practitioners seeking advanced tools for inference in diverse statistical contexts.
Reviews
No reviews yet. Be the first to review this book!