All of Statistics: A Concise Course in Statistical Inference

By Larry A. Wasserman. 2004

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Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “Further details are available in most statistics textbooks, see, e.g., Wasserman (2010).”
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “15 Result 1 (Proposition 4 in Barocas et al. [10], Theorem 17.2 in Wasserman [126]) .”
Entropy-SGD: biasing gradient descent into wide valleys (2016, methodology): “Hessian computation is in this case expensive and thus we instead compute the diagonal of the Fisher information matrix (Wasserman, 2013) using the element-wise ﬁrst and second moments of the gradients that Adam maintains, i.e., diag ( I ) = E ( g 2 ) − ( E g ) 2 where g is the back-propagated…”
MMD GAN: Towards Deeper Understanding of Moment Matching Network (2017, background): “Considering n ! 1, under mild Assumption, max M f (PX ,Pn)! 0() Pn D ! PX , where D ! means converging in distribution [3].”
An introduction to domain adaptation and transfer learning (2018, background): “For n samples that are independently drawn and identically distributed, due to the law of large numbers, the empirical risk converges to the true risk [202,32].”
Least Squares Estimation Without Priors or Supervision (2011, background): “3In particular, maximizing likelihood minimizes the Kullback-Leibler divergence between the true density and the parametric density (Wasserman, 2004).”
Adaptive Interventions Treatment Modelling and Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) and Q-Learning (2018, background): “A state model can be defined as one having sufficient statistic in a statistical sense if it can specify the relevant parameters, completely of the associated distributions with the help of comprehensive information that it should contain [65].”
Duplicate bug reports considered harmful … really? (2008, methodology): “05) [20] to verify if the accuracy using Extended Data data significantly increased.”
Confidence intervals and hypothesis testing for high-dimensional regression (2013, background): “96σ √ Qii/n] is a 95% confidence interval [Was04].”
Book Review: Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper (2009, background): “(I ﬁnd that a few chapters from All of Statistics [Wasserman 2010] and from Probabilistic Graphical Models [Koller and Friedman 2009] together with Chapter 6 of Foundations of Statistical NLP [Manning and Sch¨utze 1999] on estimation methods are useful at this stage to consolidate the mathematical u”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, background): “The null-hypothesis is only rejected if its probability is very low [Wasserman 2010].”
An introduction to g methods. (2016, background): “0 1⁄4 CovðY0;A1jZ1;A0Þ 1⁄4 CovðY;A0Þ where Covð Þ is the well-known covariance formula.(8)ðp52Þ These equalities are of little direct use for estimation, though, as they contain unobserved potential outcomes and are not yet functions of the parameters of interest.”