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An analysis of transformations
 Journal of the Royal Statistical Society. Series B (Methodological
, 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
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Cited by 1067 (3 self)
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In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal
On the Characteristic Function of the Generalized Normal Distribution
, 2009
"... of the generalized normal distribution by ..."
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation
, 2002
"... There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is ..."
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Cited by 718 (9 self)
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is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes
Why are Normal Distributions Normal?
"... Abstract We seem to be surrounded by bell curvescurves more formally known as normal distributions, or Gaussian distributions. All manner of things appear to be distributed normally: people's heights, sizes of snowflakes, errors in measurements, lifetimes of lightbulbs, IQ scores, weights of ..."
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Abstract We seem to be surrounded by bell curvescurves more formally known as normal distributions, or Gaussian distributions. All manner of things appear to be distributed normally: people's heights, sizes of snowflakes, errors in measurements, lifetimes of lightbulbs, IQ scores, weights
A model for technical inefficiency effects in a stochastic frontier production function for panel data
 Empirical Economics
, 1995
"... Abstract: A stochastic frontier production function is defined for panel data on firms, in which the nonnegative technical inetGciency effects are assumed to be a function of firmspecific variables and time. The inefficiency effects are assumed to be independently distributed as truncations of nor ..."
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Cited by 555 (4 self)
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of normal distributions with constant variance, but with means which are a linear function of observable variables. This panel data model is an extension of recently proposed models for inefTiciency effects in stochastic frontiers for crosssectional data. An empirical application of the model is obtained
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
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Cited by 736 (22 self)
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The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso
Bayesian density estimation and inference using mixtures.
 J. Amer. Statist. Assoc.
, 1995
"... JSTOR is a notforprofit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about J ..."
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Cited by 653 (18 self)
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mixtures of normal distributions. Efficient simulation methods are used to approximate various prior, posterior, and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates
On the STSP Normal Distribution
"... We introduce the standard twosided power normal distribution and consider the relation between the probability in standard twosided power distribution and the probability in standard twosided power normal distribution and obtain the even moment of the special twosided power normal distribution i ..."
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We introduce the standard twosided power normal distribution and consider the relation between the probability in standard twosided power distribution and the probability in standard twosided power normal distribution and obtain the even moment of the special twosided power normal distribution
Accurate Methods for the Statistics of Surprise and Coincidence
 COMPUTATIONAL LINGUISTICS
, 1993
"... Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used unjustifiably ..."
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Cited by 1057 (1 self)
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unjustifiably, leading to flawed results.This assumption of normal distribution limits the ability to analyze rare events. Unfortunately rare events do make up a large fraction of real text.However, more applicable methods based on likelihood ratio tests are available that yield good results with relatively
Normal distribution mapping
, 1997
"... Abstract: Normal distribution mapping is an extension of traditional texture mapping methods to normal and shading information. The surface normal direction and shading for a patch of surface are represented by a statistical distribution of normal directions. The normal distribution map is a map of ..."
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Cited by 15 (1 self)
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Abstract: Normal distribution mapping is an extension of traditional texture mapping methods to normal and shading information. The surface normal direction and shading for a patch of surface are represented by a statistical distribution of normal directions. The normal distribution map is a map
Results 1  10
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29,656