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Learning HighDensity Regions for a Generalized KolmogorovSmirnov Test in HighDimensional Data
"... Address ..."
Kolmogorov–Smirnov
, 2008
"... test as a tool to study the distribution of ultrahigh energy cosmic ray sources ..."
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test as a tool to study the distribution of ultrahigh energy cosmic ray sources
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
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Cited by 1276 (124 self)
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A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose
Minimum KolmogorovSmirnov Test Statistic Parameter Estimates
 Journal of Statistical Computation and Simulation
, 2006
"... We present and implement an algorithm for computing the parameter estimates in a univariate probability model for a continuous random variable that minimizes the Kolmogorov–Smirnov test statistic. The algorithm uses an evolutionary optimization technique to solve for the estimates. Several simulatio ..."
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Cited by 8 (1 self)
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We present and implement an algorithm for computing the parameter estimates in a univariate probability model for a continuous random variable that minimizes the Kolmogorov–Smirnov test statistic. The algorithm uses an evolutionary optimization technique to solve for the estimates. Several
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture
Prepared for submission to JCAP The KolmogorovSmirnov test for the CMB
"... Abstract. We investigate the statistics of the cosmic microwave background using the KolmogorovSmirnov test. We show that, when we correctly decorrelate the data, the partition function of the Kolmogorov stochasticity parameter is compatible with the Kolmogorov distribution and, contrary to previ ..."
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Abstract. We investigate the statistics of the cosmic microwave background using the KolmogorovSmirnov test. We show that, when we correctly decorrelate the data, the partition function of the Kolmogorov stochasticity parameter is compatible with the Kolmogorov distribution and, contrary
Powerlaw distributions in empirical data
 ISSN 00361445. doi: 10.1137/ 070710111. URL http://dx.doi.org/10.1137/070710111
, 2009
"... Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur in the t ..."
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Cited by 589 (7 self)
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estimates for powerlaw data, based on maximum likelihood methods and the KolmogorovSmirnov statistic. We also show how to tell whether the data follow a powerlaw distribution at all, defining quantitative measures that indicate when the power law is a reasonable fit to the data and when it is not. We
Validation of Bayesian posterior distributions using a multidimensional Kolmogorov–Smirnov test
, 2015
"... We extend the Kolmogorov–Smirnov (KS) test to multiple dimensions by suggesting a Rn → [0, 1] mapping based on the probability content of the highest probability density region of the reference distribution under consideration; this mapping reduces the problem back to the onedimensional case to wh ..."
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We extend the Kolmogorov–Smirnov (KS) test to multiple dimensions by suggesting a Rn → [0, 1] mapping based on the probability content of the highest probability density region of the reference distribution under consideration; this mapping reduces the problem back to the onedimensional case
KOLMOGOROVSMIRNOV STATISTICS IN MULTIPLE REGRESSION
"... tribution of KolmogorovSmirnov statistics, high breakdown point method, implicit weighting of residuals. Abstract: An asymptotic distribution of KolmogorovSmirnov statistics in linear regression model is derived. An example which was the inspiration for deriving the result is given. 1 Introduction ..."
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tribution of KolmogorovSmirnov statistics, high breakdown point method, implicit weighting of residuals. Abstract: An asymptotic distribution of KolmogorovSmirnov statistics in linear regression model is derived. An example which was the inspiration for deriving the result is given. 1
Results 1  10
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