The Bactra Review Subject Index

Probability and Statistics

A. C. Atkinson and A. N. Donev, Optimum Experimental Designs
Martin Anthony and Peter L. Bartlett, Neural Network Learning: Theoretical Foundations
Peter Bühlmann and Sara van de Geer, Statistics for High-Dimensional Data: Methods, Theory and Applications
Bent Jesper Christensen and Nicholas M. Kiefer, Economic Modeling and Inference
Gerda Claeskens and Nils Lid Hjort, Model Selection and Model Averaging
D. R. Cox and Christl A. Donnelly, Principles of Applied Statistics
Harald Cramér, Mathematical Methods of Statistics
David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World
Jianqing Fan and Qiwei Yao, Nonlinear Time Series: Nonparametric and Parametric Method
Andrew M. Fraser, Hidden Markov Models and Dynamical Systems
B. Roy Frieden, Physics from Fisher Information: A Unification
Christian Gouriéroux and Alain Monfort, Simulation-Based Econometric Methods
Mark S. Handcock and Martina Morris, Relative Distribution Methods in the Social Sciences
Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age
Paul H. Harvey and Mark D. Pagel, The Comparative Method in Evolutionary Biology
Susan Hough, Predicting the Unpredictable: The Tumultuous Science of Earthquake Prediction
Marius Iosifescu and Serban Grigorescu, Dependence with Complete Connections and Its Applications
Kurt Jacobs, Stochastic Processes for Physicists: Understanding Noisy Systems
Michael J. Kearns and Umesh V. Vazirani, An Introduction to Computational Learning Theory
Gary King, A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data
Jack L. King, Operational Risk: Measurement and Modelling
Charles F. Manski, Identification for Prediction and Decision
Rosario N. Mantegna and H. Eugene Stanley, An Introduction to Econophysics: Correlations and Complexity in Finance
Deborah G. Mayo, Error and the Growth of Experimental Knowledge
Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar, Foundations of Machine Learning
E. J. G. Pitman, Some Basic Theory for Statistical Inference
Fred Rieke, David Warland, Rob de Ruyter van Steveninck, and William Bialek, Spikes: Exploring the Neural Code
Jorma Rissanen, Stochastic Complexity in Statistical Inquiry
Robert E. Schapire and Yoav Freund, Boosting: Foundations and Algorithms
A. N. Shiryaev, Essentials of Stochastic Finance: Facts, Models, Theory
Chris Thornton, Truth from Trash: How Learning Makes Sense
V. N. Vapnik, The Nature of Statistical Learning Theory
Halbert White, Estimation, Inference, and Specification Analysis

Cf. Mathematics