All of Statistics

By Larry A. Wasserman. 2004

1,040 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Statistics and Probability

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In Biomedical Engineering

In Statistics and Probability

Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “Hence, by taking sums (see e.g., (Wasserman, 2004, Theorem 5.5)), we arrive at the desired statement.”
Generative and Latent Mean Map Kernels (2010, methodology): “For each species sampling, we optimized the bandwidth of a KDE by minimizing the integrated square error [28].”
Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System (2015, methodology): “To assess whether there was a statistically significant increase in poseinvariant information from ML/MF to AL, we ran a permutation test (Wasserman, 2004) by combining the data from ML/MF and AL into one population, randomly selected a population of neurons from this joint ML/MF-AL population that…”
The need for Bayesian hypothesis testing in psychological science (2017, background): “For instance, Burdette and Gehan (1970, p. 9) associated specific ranges of p values with varying levels of evidence (see also Wasserman, 2004, p. 157): When p > .1 this yields “little or no real evidence against the null hypothesis”; .05 < p < .1 implies “suggestive evidence against the null…”
SPADES AND MIXTURE MODELS (2009, background): “tion problems we refer to [11]. Section 4 is devoted to mixture models. A vast amount of literature exists on estimation in mixture models, especially when the number of components is known; see e.g. [36] for examples involving the EM algorithm. The literature on determining the number of mixture co”
Least Squares Estimation Without Priors or Supervision (2011, background): “(3.1) 3In particular, maximizing likelihood minimizes the Kullback-Leibler divergence between the true density and the parametric density (Wasserman, 2004).”
CrowdScreen: algorithms for filtering data with humans (2012, background): “H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information Filtering”
Statistical issues in the analysis of neuronal data. (2005, background): “To those who want mathematical details on these topics we recommend the introductory text by Wasserman (2004).”
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models (2015, methodology): “With q = pθ, this approximation is a Nadaraya-Watson kernel estimate of the conditional expectation (see, for example, Wasserman, 2004, Chapter 21).”
Bayesian Estimation and Inference (2015, background): “Therefore, even in cases where empirical or informative priors are used, results from Bayesian analysis will converge with frequentist estimation as sample sizes increase (see Wasserman, 2004, p. 181) yet still provide the advantage of the probabilistic interpretation of parameters.”