Springer Series in Statistics

Dordrecht : : Springer, , 2011.

1 online resource (677 p.)

Description based upon print version of record.. - Targeted Learning; Foreword; Foreword; Preface; The Journey; The Journey; Intended Readership; Intended Readership; Outline; Acknowledgements; Contents; List of Contributors; Abbreviations and Notation; Models, Inference, and Truth:Probabilistic Reasoning in the Information Era; Informational Metaphysics and the Science of Data; From Parmenides to Pearson; The Pearsonian Philosophy; Material Eliminativism vs. Common Sense; The Fisherean Turn: Estimation, Models and Causality; Hypothesis Testing: The Fisher-Neyman/Pearson Controversy; Bayesianism, Indirect Probability, Knowledge Representation. - 1.6 Notes and Further ReadingChapter 2 Defining the Model and Parameter; 2.1 Defining the Structural Causal Model; 2.2 Causal Graphs; 2.2.1 Terminology; 2.2.2 Assumptions; 2.3 Defining the Causal Target Parameter; 2.3.1 Interventions; 2.3.2 Counterfactuals; 2.3.3 Establishing Identifiability; 2.3.4 Commit to a Statistical Model and Target Parameter; 2.3.5 Interpretation of Target Parameter; 2.4 Revisiting the Mortality Example; 2.5 Road Map for Targeted Learning; 2.6 Conceptual Framework; 2.7 Notes and Further Reading; Chapter 3 Super Learning; 3.1 Background. - 3.2 Defining the Estimation Problem3.3 Super (Machine) Learning; 3.3.1 Discrete Super Learner; 3.3.2 Super Learner; 3.3.3 Finite Sample Performance and Applications; 3.4 Road Maps; 3.5 Conceptual Framework of Loss-Based Super Learning; 3.6 Notes and Further Reading; Chapter 4 Introduction to TMLE; 4.1 Motivation; 4.2 TMLE in Action: Mortality Study Example; 4.2.1 Estimator; 4.2.2 Inference; 4.3 Practical Implications; 4.3.1 Randomized Controlled Trials; 4.3.2 Observational Studies; 4.4 Summary; 4.5 Road Map for Targeted Learning; 4.6 Notes and Further Reading; Chapter 5 Understanding TMLE. - 5.1 Conceptual Framework5.2 Definition of TMLE in Context of the Mortality Example; 5.2.1 Estimating ̄Q0; 5.2.2 Estimating g0; 5.2.3 Determining the Efficient Influence Curve D∗(P); 5.2.4 Determining the Fluctuation Working Model; 5.2.5 Updating ̄Q0n; 5.2.6 Estimating the Target Parameter; 5.2.7 Calculating Standard Errors; 5.3 Foundation and Philosophy of TMLE; 5.4 Summary; Chapter 6 Why TMLE?; 6.1 Landscape; 6.1.1 MLE; 6.1.2 Estimating Equation Methods; 6.2 TMLE is Based on (Targeted) Loss-Based Learning; 6.2.1 Competitors; Part II Additional Core Topics. - Beyond Parametric Statistics and Maximum Likelihood Estimation: A Pragmatist PerspectiveConclusion; Part I Targeted Learning: The Basics; Chapter 1 The Open Problem; 1.1 Learning from Data; 1.2 Traditional Approach to Estimation; 1.2.1 Experimental Studies; 1.2.2 Observational Studies; 1.2.3 Regression in (Misspecified) Parametric Statistical Models; 1.2.4 The Complications of Human Art in Statistics; 1.3 Data, Model, and Target Parameter; 1.3.1 The Model; 1.3.2 The Target Parameter; 1.3.3 Summary of Concepts; 1.4 The Need for Targeted Estimators; 1.5 Road Map for Targeted Learning. - Chapter 7 Bounded Continuous Outcomes. - The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the targe

Electronic books.

519.54 . - 519.535

9781441997814