Statistics and Data Science

Invited Papers.- Symbolic Formulae for Linear Mixed Models.- code::proof: Prepare for most weather conditions.- Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models.- Flexible Modelling via Multivariate Skew Distributions.- Estimating occupancy and fitting models with the two-stage approach.- Component elimination strategies for mixtures of multiple scale distributions.- An introduction to approximate Bayesian computation.- Contributing Papers.- Truth, Proof, and Reproducibility: There's no counter-attack for the codeless.- On Adaptive Gauss-Hermite Quadrature for Estimation in GLMM's.- Deep learning with periodic features and applications in particle physics.- Copula Modelling of Nurses' Agitation-Sedation Rating of ICU Patients.- Predicting the whole distribution with methods for depth data analysis demonstrated on a colorectal cancer treatment study.- Resilient and Deep Network for Internet of Things (IoT) Malware Detection.- Prediction of Neurological Deterioration of Patients with Mild Traumatic Brain Injury using Machine Learning.- Spherical data handling and analysis with R package rcosmo.- On the Parameter Estimation in the Schwartz-Smith's Two-Factor Model.- Interval estimators for inequality measures using grouped data.- Exact model averaged tail area confidence intervals.