Probability and Statistics

Scott Ashworth, Christopher R. Berry and Ethan Bueno de Mesquita, Theory and Credibility: Integrating Theoretical and Empirical Social Science

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

John H. Goldthorpe, Sociology as a Population Science

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

Wesley C. Salmon, with Richard C. Jeffrey and Jeffrey G. Greeno, Statistical Explanation and Statistical Relevance

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