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The Effects of an Arcsin Square Root Transform on a Binomial Distributed Quantity
"... This document provides proofs of the following: • The binomial distribution can be approximated with a Gaussian distribution at large values of N. • The arcsin squareroot transform is the variance stabilising transform for the binomial distribution. • The Gaussian approximation for the binomial dis ..."
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This document provides proofs of the following: • The binomial distribution can be approximated with a Gaussian distribution at large values of N. • The arcsin squareroot transform is the variance stabilising transform for the binomial distribution. • The Gaussian approximation for the binomial
Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models
"... This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forcedchoice variables, questionanswer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arc ..."
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Cited by 252 (7 self)
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the arcsinesquareroot transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are wellsuited to analyze
An Efficient SquareRoot Algorithm for BLAST
 IEEE Trans. Sig. Proc
, 2000
"... Bell Labs Layered SpaceTime (BLAST) is a scheme for transmitting information over a richscattering wireless environment using multiple receive and transmit antennas. The main computational bottleneck in the BLAST algorithm is a "nulling and cancelation" step, where the optimal ordering f ..."
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Cited by 119 (9 self)
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for the sequential estimation and detection of the received signals is determined. To reduce the computational cost of BLAST, in this paper we develop an efficient squareroot algorithm for the nulling and cancellation step. The main features of the algorithm include efficiency: the computational cost is reduced
Ensemble Square Root Filters
, 2003
"... Ensemble data assimilation methods assimilate observations using statespace estimation methods and lowrank representations of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics ..."
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Cited by 120 (8 self)
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of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.
Removing shapepreserving transformations in squareroot elastic (sre) framework for shape analysis of curves
 In EMMCVPR’07
, 2007
"... Abstract. This paper illustrates and extends an efficient framework, called the squarerootelastic (SRE) framework, for studying shapes of closed curves, that was first introduced in [2]. This framework combines the strengths of two important ideas elastic shape metric and pathstraightening metho ..."
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Cited by 30 (9 self)
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Abstract. This paper illustrates and extends an efficient framework, called the squarerootelastic (SRE) framework, for studying shapes of closed curves, that was first introduced in [2]. This framework combines the strengths of two important ideas elastic shape metric and path
A comparison of hybrid ensemble transform Kalman filterOptimum Interpolation and ensemble squareroot filter analysis schemes
, 2007
"... A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A twolayer primitive equation model was used under perfectmodel assumptions. A simplified observation network was used ..."
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Cited by 20 (6 self)
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A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A twolayer primitive equation model was used under perfectmodel assumptions. A simplified observation network
Stochastic Volatility for Lévy Processes
, 2001
"... Three processes re°ecting persistence of volatility are initially formulated by evaluating three L¶evy processes at a time change given by the integral of a mean reverting square root process. The model for the mean reverting time change is then generalized to include NonGaussian models that are so ..."
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Cited by 209 (12 self)
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Three processes re°ecting persistence of volatility are initially formulated by evaluating three L¶evy processes at a time change given by the integral of a mean reverting square root process. The model for the mean reverting time change is then generalized to include NonGaussian models
A GaugeInvariant Formulation for Constrained Mechanical Systems Using SquareRoot Factorization and Unitary Transformation
"... A gaugeinvariant formulation for deriving the dynamic equations of constrained multibody systems in terms of ..."
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A gaugeinvariant formulation for deriving the dynamic equations of constrained multibody systems in terms of
THE UNIFORM MIXTURE OF GENERALIZED ARCSINE DISTRIBUTIONS
"... A single, tractable, special case of the problem of continuous mixtures of beta distributions over their parameters is considered. This is the uniform mixture of generalized arcsine distributions which, curiously, turns out to be linked by transformation to the Cauchy distribution. AMS 2000 subject ..."
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A single, tractable, special case of the problem of continuous mixtures of beta distributions over their parameters is considered. This is the uniform mixture of generalized arcsine distributions which, curiously, turns out to be linked by transformation to the Cauchy distribution. AMS 2000
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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from pixel to pixel and there is noise correlation between pixels. For similar reasons the noise level does not have the common squareroot dependence on the number of samples taken. In the case of Cartesian sampling with reconstruction as initially explained, this can be made yet clearer. For one
Results 1  10
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