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Pseudo-value regression trees
This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our...
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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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An Overview of Discrete Distributions in Modelling COVID-19 Data Sets
The mathematical modeling of the coronavirus disease-19 (COVID-19) pandemic has been attempted by a large number of researchers from the very...
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Aspects of robust canonical correlation analysis, principal components and association
Principal component analysis (PCA) and canonical correlation analysis (CCA) are dimension-reduction techniques in which either a random vector is...
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Correlation and Regression
Correlation and regression are the techniques which are used to investigate if there is a relationship between two quantitative variables....
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The main contributions of robust statistics to statistical science and a new challenge
In the first part of the paper, we trace the development of robust statistics through its main contributions which have penetrated mainstream...
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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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Sentiment Analysis
This chapter presents the theory and practical applications in Stata, R, and Python of the so-called sentiment analysis (SA), a subfield of Machine...
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Interim Compliance Tests
Compliance tests determine whether the firm’s transaction processing follows generally accepted accounting principles (GAAP). They have grown...
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Functional Linear Models for the Analysis of Similarity of Waveforms
In seismology methods based on waveform similarity analysis are adopted to identify sequences of events characterized by similar fault mechanism and...
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Bayesian Latent Gaussian Models
Bayesian latent Gaussian models are Bayesian hierarchical models that assign Gaussian prior densities to the latent parameters. In this chapter, we...
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The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction
Gaussian process (GP)-based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that...
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History of the Statistical Design of Agricultural Experiments
In Section 1 the approach of improving crop yields by the development of agriculture and addition of various mineral or organic substances in the...
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Conditional feature importance for mixed data
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely...
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Run and Scan Rules in Statistical Process Monitoring
In this paper, we provide an overview of the use of run and scan rules in statistical process monitoring. Although we focus on control charts,...
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The Basics of Machine Learning
This chapter offers a general introduction to the rationale and ontology of Machine Learning (ML). It starts by discussing the definitionMachine...
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Adaptive Exponential Power Depth with Application to Classification
Depth functions have many applications in multivariate data analysis, including discriminant analysis and classification. In this paper, we introduce...
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Systematic Statistical Analysis of Microbial Data from Dilution Series
In microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating...
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Mixed-effect models with trees
Tree-based regression models are a class of statistical models for predicting continuous response variables when the shape of the regression function...
