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Important Packages
R packages are extensions to the R programming language, and these are collections of R functions, complied code, sample data, and documentation in a...
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Alternative Machine Learning Methods for Credit Card Default Forecasting*
Following de Mello and Ponti (Machine learning: a practical approach on the statistical learning theory. Springer, 2018), Bzdok et al. (Nat Methods...
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Predicting Housing Prices for Spanish Regions
This paper aims to forecast the long-term trend of housing prices in the Spanish cities with more than 25,000 inhabitants, a total of 275 individual...
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Potential to Density via Poisson Equation: Application to Bespoke Learning of Gravitational Mass Density in Real Galaxy
In multiple real-world dynamical systems, structural properties can be deterministically linked to the evolution-driving function. For example, in...
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Introduction to Business Data Analytics: Setting the Stage
Spoiler-alert: Business Data Analytics (BDA), the focus of this book, is solely concerned with one task, and one task only: to provide the richest...
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A Dynamic Individual-Based Model for High-Resolution Ant Interactions
Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there...
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Gradient boosting with extreme-value theory for wildfire prediction
This paper details the approach of the team Kohrrelation in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire...
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Estimation of semiparametric varying-coefficient spatial autoregressive models with missing in the dependent variable
This paper investigates estimation of semiparametric varying-coefficient spatial autoregressive models in which the dependent variable is missing at...
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General dependence structures for some models based on exponential families with quadratic variance functions
We describe a procedure to introduce general dependence structures on a set of random variables. These include order- q moving average-type...
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Deep Learning
This chapter presents deep learningDeep learning algorithms, a subset of machine learning methods built on sophisticated multi-layer artificial...
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Probabilistic Forecasts of Arctic Sea Ice Thickness
In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea...
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Lagrangian Spatio-Temporal Nonstationary Covariance Functions
The Lagrangian reference frame has been used to model spatio-temporal dependence of purely spatial second-order stationary random fields that are...
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Conducting a Dynamic Microsimulation for Care Research: Data Generation, Transition Probabilities and Sensitivity Analysis
This contribution providesBurgard, Jan Pablo insights on a novel dynamic microsimulation modelKrause, Joscha that is developed within the...
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Time Series
A time series is a collection of data points ordered chronologically and recorded at successive time intervals. These data points can be taken over...
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Modelling interaction patterns in a predator-prey system of two freshwater organisms in discrete time: an identified structural VAR approach
In ecology, the concept of predation describes interdependent patterns of having one species (called the predator) killing and consuming another (the...
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Gaussian Processes and Model Emulation
Sampling-based estimation of the posterior distribution is computationally demanding. We have already mentioned the continuing search for efficient...
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Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas
Recently, the petroleum industry has faced the era of data explosion, and many oil and gas companies resort to data-driven approaches for...
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An Efficient Nonparametric Estimate for Spatially Correlated Functional Data
Functional data are often generated by modern biomedical technologies where features related to the pathophysiology and pathogenesis of a disease are...
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Non-Linear and Non-Gaussian State Space Models
This chapter discusses estimation for non-linear and non-Gaussian state space methods. We start by defining conditionally Gaussian and more general...
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Two Gaussian Regularization Methods for Time-Varying Networks
We model time-varying network data as realizations from multivariate Gaussian distributions with precision matrices that change over time. To...
