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Statistical Learning: Concepts
We introduce statistical learning. We distinguish between supervised and unsupervised learning, classification, and regression and between the goals...
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Applied Statistical Learning With Case Studies in Stata
This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical...
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Statistical guarantees for sparse deep learning
Neural networks are becoming increasingly popular in applications, but our mathematical understanding of their potential and limitations is still...
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A statistical learning view of simple Kriging
In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibiting a possibly complex spatial dependence...
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Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several...
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An Introduction to Statistical Learning with Applications in Python
An Introduction to Statistical Learningprovides an accessible overview of the field of statistical learning, an essential toolset for making sense...
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Statistical Learning: Practical Aspects
After the largely conceptual considerations in the first chapter, this chapter turns to some of the more practical aspects of statistical learning....
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Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is...
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Multivariate understanding of income and expenditure in United States households with statistical learning
In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in...
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Statistical Learning in Genetics An Introduction Using R
This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology...
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Statistical Learning
In order to motivate our study of statistical learning, we begin with a simple example. Suppose that we are statistical consultants hired by a client...
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Statistical learning for species distribution models in ecological studies
We discuss species distribution models (SDM) for biodiversity studies in ecology. SDM plays an important role to estimate abundance of a species...
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Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data
Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end,...
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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications Selected Contributions from SimStat 2019 and Invited Papers
This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and...
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Simultaneous Learning the Dimension and Parameter of a Statistical Model with Big Data
Estimating the dimension of a model along with its parameters is fundamental to many statistical learning problems. Traditional model selection...
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A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes
Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge, we propose a method based on statistics and machine learning for the spatial...
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Minimum Divergence Methods in Statistical Machine Learning From an Information Geometric Viewpoint
This book explores minimum divergence methods of statistical machine learning for estimation, regression, prediction, and so forth, in which we...
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Statistical Audit Design
The purpose of this chapter is to provide guidance in the implementation of statistical audit procedures. The following pages outline the statistical...
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Statistical Learning for Change Point and Anomaly Detection in Graphs
Complex systems which can be represented in the form of static and dynamic graphs arise in different fields, e.g., communication, engineering and...