3 D Statistical Learning

Case Study: Predictive Maintenance for Medical Devices

1. Problem Description and Objective We aim to anticipate equipment failure in 75 medical devices using multivariate longitudinal telemetry data. We develop and evaluate a predictive model, document our approach, and implement an early warning…

Introduction to Statistical Decision Theory – Edition 1: From Traditional Statistical Methods to Decision Theory

1. Basic Idea Statistics is the science of learning from data. It has two main purposes: To describe what has been observed. To make inferences about the wider world beyond the observed data. Often, we…

Introduction to Statistical Decision Theory – Edition 2: Foundations of Estimation and Hypothesis Testing

1. Point Estimation Example Estimating Variance in the Normal Model Let \(X_1, \ldots, X_n \sim N(\mu, \sigma^2)\). We are interested in estimating \(\theta = \sigma^2\). Well-known estimators: Sample variance (unbiased): $$\hat{\sigma}^2 = \frac{1}{n-1} \sum_{i=1}^n (X_i…

Introduction to Statistical Decision Theory – Edition 3: Ethical and Strategic Decisions Under Uncertainty

This third edition presents a real-world-inspired decision-theoretic scenario rooted in the classical formulation of I. J. Good (1952), where human incentives, ethical dilemmas, and corporate strategy intersect. A new invention is presented to a company’s…

Making Bayesian Statistics Accessible to Everyone – Edition 2: Motivating Examples to Deepen Intuition

Introduction In the first edition of this series, we introduced the fundamental distinction between the frequentist and Bayesian paradigms, using the estimation of a population mean as a guiding example. We explored how the Bayesian…

Introduction to Statistical Decision Theory- Edition 4: Cold Weather, Warm Decisions

A Practical Decision Under Uncertainty: Heating or Buying a Pullover? In this fourth edition, we analyze a relatable and simple decision problem using the framework of statistical decision theory. The goal is to determine the…