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Bahadur representations for the bootstrap median absolute deviation and the application to projection depth weighted mean
Median absolute deviation (hereafter MAD) is known as a robust alternative to the ordinary variance. It has been widely utilized to induce robust...
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A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling
Personalized risk prediction calculators abound in medicine, and they carry important information about the effect of prognostic factors on outcomes...
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Robust Regression Estimators
A fundamental goal is understanding the nature of the association between some variable Y and a collection of explanatory variables...
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Ensemble of optimal trees, random forest and random projection ensemble classification
The predictive performance of a random forest ensemble is highly associated with the strength of individual trees and their diversity. Ensemble of a...
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Interpretability via Random Forests
Although there is no consensus on a precise definition of interpretability, it is possible to identify several requirements: “simplicity, stability,...
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An Approach for Specifying Trimming and Winsorization Cutoffs
Outliers and extreme values are common in the era of big data , especially in the collection of survey data and real analysis. Clearly, care needs to...
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Supervised classification of curves via a combined use of functional data analysis and tree-based methods
Technological advancement led to the development of tools to collect vast amounts of data usually recorded at temporal stamps or arriving over time,...
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Experimental Data Analysis
The technical and more extensive part of this chapter describes how to apply statistical and probabilistic methods to the various types of...
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Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable...
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Statistical Data Mining of Clinical Data
This chapter provides an introduction into the diverse field of data mining, as viewed from the perspective of a clinical statistician. We start with...
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Feedforward Neural Networks
This chapter provides a more in-depth description of supervised learning, deep learning, and neural networks—presenting the foundational mathematical...
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A Log-Linear Model for Inference on Bias in Microbiome Studies
Microbiome sequencing data are known to be biased; the measured taxa relative abundances can be systematically distorted from their true values at...
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A bias-variance analysis of state-of-the-art random forest text classifiers
Random forest (RF) classifiers do excel in a variety of automatic classification tasks, such as topic categorization and sentiment analysis. Despite...
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Regression and Correlation
The general objective of a regression analysis is to investigate the relationship between two (or more) variables so that we can gain information...
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Residuals
Residuals summarize the variation and can be used to estimate parameters, identify outliers and identify influential observations. For the bilinear...
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Plug-in marginal estimation under a general regression model with missing responses and covariates
In this paper, we consider a general regression model where missing data occur in the response and in the covariates. Our aim is to estimate the...
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Solutions
This chapter presents potential solutions to the exercises presented in the previous chapters, along with additional discussion of related issues....
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The Recent History of Statistics: Comparing Temporal Patterns of Word Clusters
The abstracts published by the Journal of the American Statistical Association in the time span 1946–2016 have been examined in order to identify...
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Fast Community Detection in Complex Networks with a K-Depths Classifier
We introduce a notion of data depth for recovery of community structures in large complex networks. We propose a new data-driven algorithm, K-depths,...
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Compositional Analysis of Microbiome Data
This chapter focuses on compositional analysisCompositional analysis of microbiome data. In Sect. 10.1, we introduce the concepts, principles,...