Spatial Predictive Modeling with R

The paper presents novel hybrid methods for spatial predictive modeling using mathematical and geostatistical methods, with variable selection techniques and parameter/model optimization for improved predictive accuracy. It provides reproducible examples in R.

Format: Hardback
Length: 384 pages
Publication date: 28 February 2022
Publisher: Taylor & Francis Ltd

Here is the rephrased text:

The Statistical Process Monitoring (SPM) process encompasses a range of essential components, each of which is carefully introduced in this comprehensive guide. We delve into novel hybrid methods, comprising a staggering 228 hybrids and numerous variants, which combine modern statistical methods or machine learning techniques with mathematical and/or univariate geostatistical methods. These innovative approaches offer enhanced capabilities for data analysis and prediction.

Furthermore, we explore novel predictive accuracy-based variable selection techniques tailored for spatial predictive methods. These techniques aim to optimize the selection of variables, ensuring optimal accuracy in model predictions.

Additionally, we introduce parameter/model optimization approaches based on predictive accuracy, allowing for refinement and improvement of statistical models. Reproducible examples in R, covering a diverse range of data types, illustrate the practical application of SPM techniques, making it easier for practitioners to implement and leverage these powerful tools in their data analysis endeavors.

Weight: 907g
Dimension: 254 x 178 (mm)
ISBN-13: 9780367550547