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Use of Real-World EMR Data to Rapidly Evaluate Treatment Effects of Existing Drugs for Emerging Infectious Diseases: Remdesivir for COVID-19 Treatment as an Example
For an emerging infectious disease such as 2019 coronavirus disease (COVID-19), initially there may not be any existing medication or treatment...
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Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model
Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur...
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Nonparametric Regression
The main goal Nonparametric regression Smoothingof nonparametric regression is the flexible modeling of effects of continuous covariates on a...
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Outlier Identification for Symbolic Data with the Application of the DBSCAN Algorithm
Outliers have a significant negative impact on the data quality, data analysis results. If a large dataset contains only few outliers it is essential...
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MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Map** Models
Exploring spatial patterns in the context of disease map** is a decisive approach to bring evidence of geographical tendencies in assessing disease...
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Building Predictive Models with Machine Learning
This chapter functions as a practical guide for constructing predictive models using machine learning, focusing on the nuanced process of translating...
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Classification Models
One of the main problems that often occur in data analytics is assigning a category to each data record. These kinds of problems are very common in...
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Classification with Supervised Learning Methods
I covered modeling as a way to predict either future events (i.e., forecasting) or the outcome of decisions (i.e., predicting) in the previous...
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Variable selection for multivariate functional data via conditional correlation learning
Variable selection involves selecting truly important predictors from p -dimensional multivariate functional predictors in functional predictive...
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Mixture copulas with discrete margins and their application to imbalanced data
This article introduces the approach of using Bayesian sampling to estimate the mixture copula with discrete margins, we further apply our models to...
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Graphical Modeling of Multiple Biological Pathways in Genomic Studies
Complex diseases are associated with a variety of genomic factors. Identifying such risk factors can help us to better understand the pathogenesis of...
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Testing for conditional independence of survival time from covariate
This study examined the test of independence of survival time from a covariate in a more general setting using empirical process techniques. Previous...
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Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima–Peru
Peru is an emerging nation with a nonuniform development where the growth is focused on some specific cities and districts, as a result there is...
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Automatic Machine Learning-Based OLAP Measure Detection for Tabular Data
Nowadays, it is difficult for companies and organisations without Business Intelligence (BI) experts to carry out data analyses. Existing automatic...
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Some Notes on Types of Symmetry for Crossover Designs
Crossover designs are used to assign multiple treatments to the same unit over a period of time. In the search of optimal crossover designs,...
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A review on design inspired subsampling for big data
Subsampling focuses on selecting a subsample that can efficiently sketch the information of the original data in terms of statistical inference. It...
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Imbalanced Data and Resampling Techniques
The SPSS Modeler helps us to build statistical models to predict certain variables. These variables can be that, e.g., a customer buys a product or...
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Lasso and Friends
Regularized linear models are generalized linear regression models with a penalty for large coefficients to regulate the bias-variance tradeoff. For...
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Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection
Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock...
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Accelerated Sequential Data Clustering
Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data,...
