Targeted Learning
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 th…
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- Full title :Targeted Learning
Summary
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.
Read More ↓Edition Statements
- Publication Date:2011.
Publication Statements
- Publisher(s):Springer New York
- Place of Publication:New York, NY
- Language:eng
- Publication Date:2011.
Notes
Chapter 7 Bounded Continuous Outcomes
The Physical Object
- Description:1 online resource (677 pages)
Edition Identifiers
- ISBN:1441997814, 9781441997814
- DOI:10.1007/978-1-4419-9782-1
- oclc:ocn990739547
- LC Classification:HF5549.5.T7
- Dewey Decimal:519.54
Table of Contents
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
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
1.6 Note…
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