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Normal Scores, Normal Plots Tests for Normality
Journal of the American Statistical Association, 1996Abstract In this article we develop new plotting positions for normal plots. The use of the plots usually centers on detecting irregular tail behavior or outliers. Along with the normal plot, we develop tests for various departures from normality, especially for skewness and heavy tails.
B. M. Brown, T. P. Hettmansperger
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Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004
Making decisions based on a linear combination L of features is of course very common in pattern recognition. For distinguishing between two hypotheses or classes, the test is of the form sign (L - /spl tau/) for some threshold /spl tau/. Due mainly to fixing /spl tau/, such tests are sensitive to changes in illumination and other variations in imaging
Sachin Gangaputra, Donald Geman
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Making decisions based on a linear combination L of features is of course very common in pattern recognition. For distinguishing between two hypotheses or classes, the test is of the form sign (L - /spl tau/) for some threshold /spl tau/. Due mainly to fixing /spl tau/, such tests are sensitive to changes in illumination and other variations in imaging
Sachin Gangaputra, Donald Geman
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1986
Before setting out on a discussion of tests for normality, we must consider briefly the consequences of non-normality. Clearly, if we know that the distribution of the disturbances is, say gamma, then a different model, and different methods, should be used than those discussed in this volume.
G. Barrie Wetherill +5 more
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Before setting out on a discussion of tests for normality, we must consider briefly the consequences of non-normality. Clearly, if we know that the distribution of the disturbances is, say gamma, then a different model, and different methods, should be used than those discussed in this volume.
G. Barrie Wetherill +5 more
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Tests for normality versus lognormality
Communications in Statistics, 1975In applied statistics two of the most widely used distributions for continuous random variables are the normal and the lognormal. In this paper we consider the problem of selecting one of these two distributions. Each distribution is allowed to have unknown location and scale parameters and the lognormal has an unknown shape parameter in addition ...
Klimko, L. A. +2 more
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Statistica Neerlandica, 1971
Summary An analogue of the Cramer‐von MismW2‐statistic is given for testing the composite hypothesis of normality with unspecified parameters. Some Monte Carlo percentiles of this statistic are provided. A power comparison with the test developed in [4] shows that the present test is better against certain alternatives.
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Summary An analogue of the Cramer‐von MismW2‐statistic is given for testing the composite hypothesis of normality with unspecified parameters. Some Monte Carlo percentiles of this statistic are provided. A power comparison with the test developed in [4] shows that the present test is better against certain alternatives.
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Journal of Quality Technology, 1983
A FORTRAN computer program is presented that performs a simple test for normality based on the fact that the mean and variance of a sample are independent if and only if the underlying population is normal. The program provides the approximate descripti..
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A FORTRAN computer program is presented that performs a simple test for normality based on the fact that the mean and variance of a sample are independent if and only if the underlying population is normal. The program provides the approximate descripti..
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1988
The Central Limit Theorem and other limit theorems of statistics provide the basis for the centrality of the normal distribution in statistical inference. These theorems tell us that, regardless of the underlying distribution of the data, as the sample size on which they are based gets very large the sampling distribution of statistics calculated from ...
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The Central Limit Theorem and other limit theorems of statistics provide the basis for the centrality of the normal distribution in statistical inference. These theorems tell us that, regardless of the underlying distribution of the data, as the sample size on which they are based gets very large the sampling distribution of statistics calculated from ...
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2014
For testing normality with unknown parameters mu and sigma{2}, extensive simulation studies have shown the superiority of the Shapiro-Wilk test and variants. The Shapiro-Francia test is a simplified version of the Shapiro-Wilk test, which uses the squared normal probability plot correlation as a test statistic. The Cramer-von Mises and Anderson-Darling
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For testing normality with unknown parameters mu and sigma{2}, extensive simulation studies have shown the superiority of the Shapiro-Wilk test and variants. The Shapiro-Francia test is a simplified version of the Shapiro-Wilk test, which uses the squared normal probability plot correlation as a test statistic. The Cramer-von Mises and Anderson-Darling
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Normalized Restricted Random Testing
2003Restricted Random Testing (RRT) is a new method of testing software that improves upon traditional random testing (RT) techniques. This paper presents new data in support of the efficiency of RRT, and presents a variation of the algorithm, Normalized Restricted Random Testing (NRRT).
Kwok Ping Chan +2 more
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Testing Normality in Autoregressive Models
Biometrika, 1985It is shown that any test of normality computed from autoregressive residuals has the same limiting null distribution as for the standard case of independent, identically distributed observations with estimated parameters. Some numerical results are given to indicate that this approximation is acceptable for sample size 20 in first- and second-order ...
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