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tm - Technisches Messen, 2019
Abstract In metrology, the normal distribution is often taken for granted, e. g. when evaluating the result of a measurement and its uncertainty, or when establishing the equivalence of measurements in key or supplementary comparisons. The correctness of this inference and subsequent conclusions is dependent on the normality assumption ...
Katy Klauenberg, Clemens Elster
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Abstract In metrology, the normal distribution is often taken for granted, e. g. when evaluating the result of a measurement and its uncertainty, or when establishing the equivalence of measurements in key or supplementary comparisons. The correctness of this inference and subsequent conclusions is dependent on the normality assumption ...
Katy Klauenberg, Clemens Elster
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Biometrika, 1992
We offer an easy-to-use multivariate adaptation of the Lin & Mudholkar (1980) z-test of univariate ...
Mudholkar, Govind S. +2 more
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We offer an easy-to-use multivariate adaptation of the Lin & Mudholkar (1980) z-test of univariate ...
Mudholkar, Govind S. +2 more
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Using OLS to test for normality [PDF]
Statistics & Probability Letters, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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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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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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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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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