Charles Stein (statistician)
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Charles Stein (statistician)
Charles Max Stein (March 22, 1920 – November 24, 2016) was an American mathematical statistician and professor of statistics at Stanford University. He received his Ph.D in 1947 at Columbia University with advisor Abraham Wald. He held faculty positions at Berkeley and the University of Chicago before moving permanently to Stanford in 1953. He is known for Stein's paradox in decision theory, which shows that ordinary least squares estimates can be uniformly improved when many parameters are estimated; for Stein's lemma, giving a formula for the covariance of one random variable with the value of a function of another when the two random variables are jointly normally distributed; and for Stein's method, a way of proving theorems such as the Central Limit Theorem that does not require the variables to be independent and identically distributed. He was a member of the National Academy of Sciences. He died in November 2016 at the age of 96. Works *''Approximate Computatio ...
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Joint Normal Distribution
In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional ( univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be ''k''-variate normally distributed if every linear combination of its ''k'' components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables each of which clusters around a mean value. Definitions Notation and parameterization The multivariate normal distribution of a ''k''-dimensional random vector \mathbf = (X_1,\ldots,X_k)^ can be written in the following notation: : \mathbf\ \sim\ \mathcal(\boldsymbol\mu,\, \boldsymbol\Sigma), or to make it explicitly known that ''X'' ...
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Members Of The United States National Academy Of Sciences
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Columbia University Alumni
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American Mathematicians
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2016 Deaths
This is a list of deaths of notable people, organised by year. New deaths articles are added to their respective month (e.g., Deaths in ) and then linked here. 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 See also * Lists of deaths by day The following pages, corresponding to the Gregorian calendar, list the historical events, births, deaths, and holidays and observances of the specified day of the year: Footnotes See also * Leap year * List of calendars * List of non-standard ... * Deaths by year {{DEFAULTSORT:deaths by year ...
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1920 Births
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Stein Discrepancy
A Stein discrepancy is a statistical divergence between two probability measures that is rooted in Stein's method. It was first formulated as a tool to assess the quality of Markov chain Monte Carlo samplers,J. Gorham and L. Mackey. Measuring Sample Quality with Stein's Method. Advances in Neural Information Processing Systems, 2015. but has since been used in diverse settings in statistics, machine learning and computer science. Definition Let \mathcal be a measurable space and let \mathcal be a set of measurable functions of the form m : \mathcal \rightarrow \mathbb. A natural notion of distance between two probability distributions P, Q, defined on \mathcal, is provided by an integral probability metric : (1.1) \quad d_(P , Q) := \sup_ , \mathbb_(X)- \mathbb_(Y) , where for the purposes of exposition we assume that the expectations exist, and that the set \mathcal is sufficiently rich that (1.1) is indeed a metric on the set of probability distributions on \mathcal, ...
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Stein's Unbiased Risk Estimate
In statistics, Stein's unbiased risk estimate (SURE) is an unbiased estimator of the mean-squared error of "a nearly arbitrary, nonlinear biased estimator." In other words, it provides an indication of the accuracy of a given estimator. This is important since the true mean-squared error of an estimator is a function of the unknown parameter to be estimated, and thus cannot be determined exactly. The technique is named after its discoverer, Charles Stein. Formal statement Let \mu \in ^d be an unknown parameter and let x \in ^d be a measurement vector whose components are independent and distributed normally with mean \mu_i, i=1,...,d, and variance \sigma^2. Suppose h(x) is an estimator of \mu from x, and can be written h(x) = x + g(x), where g is weakly differentiable. Then, Stein's unbiased risk estimate is given by :\operatorname(h) = d\sigma^2 + \, g(x)\, ^2 + 2 \sigma^2 \sum_^d \frac g_i(x) = -d\sigma^2 + \, g(x)\, ^2 + 2 \sigma^2 \sum_^d \frac h_i(x), where g_i(x) is t ...
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James–Stein Estimator
The James–Stein estimator is a biased estimator of the mean, \boldsymbol\theta, of (possibly) correlated Gaussian distributed random vectors Y = \ with unknown means \. It arose sequentially in two main published papers, the earlier version of the estimator was developed by Charles Stein in 1956, which reached a relatively shocking conclusion that while the then usual estimate of the mean, or the sample mean written by Stein and James as (Y_i) = , is admissible when m \leq 2, however it is inadmissible when m \geq 3 and proposed a possible improvement to the estimator that shrinks the sample means towards a more central mean vector \boldsymbol\nu (which can be chosen a priori or commonly the "average of averages" of the sample means given all samples share the same size), is commonly referred to as Stein's example or paradox. This earlier result was improved later by Willard James and Charles Stein in 1961 through simplifying the original process. It can be shown that t ...
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Statistical Science
''Statistical Science'' is a review journal published by the Institute of Mathematical Statistics. The founding editor was Morris H. DeGroot, who explained the mission of the journal in his 1986 editorial: "A central purpose of ''Statistical Science'' is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a modest technical level accessible to the wide community of practitioners, teachers, researchers and students of statistics and probability." Editors * 2017-2019 Cun-Hui Zhang * 2014-2016 Peter Green (statistician), Peter Green * 2011-2013 Jon Wellner * 2008-2010 David Madigan * 2005-2007 Ed George * 2002-2004 George Casella * 2001 Morris Eaton * 2001 Richard Tweedie * 1998-2000 Leon Gleser * 1995-1997 Paul Switzer * 1992-1994 Rob Kass, Robert E. Kass * 1989-1991 Carl Morris (statistician), Carl N. Morris * 1985-1989 Morris H. DeGroot References Further rea ...
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