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Construction of SkewNormal Random Variables: Are They Linear Combinations of Normal and HalfNormal?
"... Skewnormal distributions extend the normal distributions through a shape parameter α; they reduce to the standard normal random variable Z for α = 0 and to Z  or the halfnormal when α → ∞. In spite of the skewness they (dis)inherit some properties of normal random variables: Square of a skewnor ..."
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Cited by 3 (0 self)
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Skewnormal distributions extend the normal distributions through a shape parameter α; they reduce to the standard normal random variable Z for α = 0 and to Z  or the halfnormal when α → ∞. In spite of the skewness they (dis)inherit some properties of normal random variables: Square of a skewnormal
Comparison of Different Techniques to Generate Normal Random Variables ∗
, 2002
"... This exercise aims at exploring different techniques for creating a random variable X according to a normal distribution with zero mean and unit variance. The methods include the use of an inverse cumulative distribution function, the Box–Muller method, the polar technique and the application of the ..."
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Cited by 2 (0 self)
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This exercise aims at exploring different techniques for creating a random variable X according to a normal distribution with zero mean and unit variance. The methods include the use of an inverse cumulative distribution function, the Box–Muller method, the polar technique and the application
0 Error Statistics for Normal Random Variables
, 1975
"... Approved for public release; distribition unlimited. Reproduced b', NATIONAL TECHNICAL INFORMATION SERVICE US..opartmenm of Commerce ..."
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Approved for public release; distribition unlimited. Reproduced b', NATIONAL TECHNICAL INFORMATION SERVICE US..opartmenm of Commerce
INVESTIGATION OF:QUOTIENTS ' AND PRODUCTS ’ OF TWO NORMAL RANDOM. VARIABLES
"... Investigation of quotients and products of two normal random ..."
Lower convex order bound approximations for sums of logskew normal random variables
, 2008
"... When it comes to modeling dependent random variables, not surprisingly, the multivariate normal distribution has received the most attention because of its many appealing properties. However, when it comes to practical implementation, the same family of distribution is often rejected for modeling fi ..."
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When it comes to modeling dependent random variables, not surprisingly, the multivariate normal distribution has received the most attention because of its many appealing properties. However, when it comes to practical implementation, the same family of distribution is often rejected for modeling
Tail behavior of sums and differences of lognormal random variables, available at arXiv:1309.3057
, 2013
"... This article is dedicated to the memory of Peter Laurence We present sharp tail asymptotics for the density and the distribution function of linear combinations of correlated lognormal random variables, that is, exponentials of components of a correlated Gaussian vector. The asymptotic behavior tur ..."
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Cited by 7 (1 self)
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This article is dedicated to the memory of Peter Laurence We present sharp tail asymptotics for the density and the distribution function of linear combinations of correlated lognormal random variables, that is, exponentials of components of a correlated Gaussian vector. The asymptotic behavior
An introduction to variable and feature selection
 Journal of Machine Learning Research
, 2003
"... Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. ..."
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Cited by 1283 (16 self)
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Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.
PROBABILITY INEQUALITIES FOR SUMS OF BOUNDED RANDOM VARIABLES
, 1962
"... Upper bounds are derived for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt. It is assumed that the range of each summand of S is bounded or bounded above. The bounds for Pr(SES> nt) depend only on the endpoints of the ranges of the s ..."
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Cited by 2217 (2 self)
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Upper bounds are derived for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt. It is assumed that the range of each summand of S is bounded or bounded above. The bounds for Pr(SES> nt) depend only on the endpoints of the ranges
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
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Cited by 664 (14 self)
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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing
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3,312,886