"Advances in Statistical Methodologies and Their Application to Real Problems" ed. by Tsukasa Hokimoto

This book provides a cross-disciplinary forum for exploring the variety of new data analysis techniques emerging from different fields.

In recent years, statistical techniques and methods for data analysis have advanced significantly in a wide range of research areas. These developments enable researchers to analyze increasingly large datasets with more flexibility and also more accurately estimate and evaluate the phenomena they study. We recognize the value of recent advances in data analysis techniques in many different research fields. However, we also note that awareness of these different statistical and probabilistic approaches may vary, owing to differences in the datasets typical of different research fields.

Contents
Preface
1 Why the Decision‐Theoretic Perspective Misrepresents Frequentist Inference: Revisiting Stein’s Paradox and Admissibility
2 A Comparison Study on Performance of an Adaptive Filter with Other Estimation Methods for State Estimation in High-Dimensional System
3 Applications of the H-Principle of Mathematical Modelling
4 Signal Optimal Smoothing by Means of Spectral Analysis
5 Information‐Theoretic Clustering and Algorithms
6 Gamma-Kumaraswamy Distribution in Reliability Analysis: Properties and Applications
7 Nonlinear Transformations and Radar Detector Design
8 Distributions and Composite Models for Size-Type Data
9 Modelling Limit Order Book Volume Covariance Structures
10 A Practical Approach to Evaluating the Economic and Technical Feasibility of LED Luminaires
11 Validation of Instrument Measuring Continuous Variable in Medicine
12 On Decoding Brain Electrocorticography Data for Volitional Movement Intention Prediction: Theory and On-Chip Implementation
13 The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches
14 A Statistic Method for Anatomical and Evolutionary Analysis
15 Descriptive and Inferential Statistics in Undergraduate Data Science Research Projects