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Goodness-of-fit of statistical distributions
Encyclopedia with Semantic Computing and Robotic Intelligence, 2018Goodness-of-fit is used for the evaluation a model. They are commonly used to compare among competing models. The material is mostly classic. For more on the subject the reader is referred to the References including the two revised volumes Bickel and Docksum (2016).
Joseph R. Barr, Shelemyahu Zacks
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GOODNESS OF FIT FOR THE BINOMIAL DISTRIBUTION
Australian Journal of Statistics, 1997SummaryGoodness of fit testing for the binomial distribution can be carried out using Pearson's X2p statistic and its components. Applications of this technique are considered and compared with recently suggested empirical distribution function tests. Diagnostic use of components is discussed.
Best, D. J., Rayner, J. C. W.
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Convergence in Distribution for Best-Fit Decreasing
SIAM Journal on Computing, 1996Let \(X_1,X_2,\dots,X_n\) be independent random variables, uniformly distributed over \([0,1]\), that represent items sizes and let \(B_n\) be the number of bins needed to pack items of these sizes using the best-fit decreasing algorithm. The authors prove that the sequence of random variables \(n^{-1/2}(B_n-{n\over 2})\), \(n\geq 1\), converges in ...
Wansoo T. Rhee, Michel Talagrand
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Fitness Distributions and GA Hardness
2004Considerable research effort has been spent in trying to formulate a good definition of GA-Hardness. Given an instance of a problem, the objective is to estimate the performance of a GA. Despite partial successes current definitions are still unsatisfactory.
Yossi Borenstein, Riccardo Poli
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Distribution fitting approach to application fitness assessment
2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC), 2016On top of assessing the specification compliance, it is also important to verify the behavior and performance of the electronic components in the targeted application. This is usually achieved by jointly simulating the component and the application. There is a particular interest in finding the application yield caused by the process variation of the ...
Alexandra Iosub +5 more
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Fitting the Negative Binomial Distribution
Biometrics, 1986This note is a reaction to recent papers in this journal by Willson, Folks, and Young (1984) and Bowman (1984). For the biometrical analysis of certain kinds of observations, such as insect counts, accident counts, or cave entrance counts, when only nonnegative integers are observable, it is expedient to restrict attention to those random variables ...
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Goodness‐of‐fit tests for the hyperbolic distribution
Canadian Journal of Statistics, 2001AbstractThe authors give tests of fit for the hyperbolic distribution, based on the Cramér‐von Mises statistic W2. They consider the general case with four parameters unknown, and some specific cases where one or two parameters are fixed. They give two examples using stock price data.
Puig, Pedro, Stephens, Michael A.
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Exact tests of fit for a poisson distribution
Computing, 1983An algorithm is presented which will perform exact goodness-of-fit tests for a Poisson distribution. The algorithm has been implemented as a FORTRAN IV subroutine.
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Fitting Dimensional Distributions
2011This chapter focuses on the use of statistical tools for fitting models to dimensional data that represent a sample of trees. Here, we consider the models, theory, and tools for one-dimensional data, such as diameter distributions. Two-dimensional data, such as the classical allometric relationships, and multi-dimensional data, which require systems of
Andrew P. Robinson, Jeff D. Hamann
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1995
Abstract Categorical data models have traditionally been used to study nominal, and ordinal, response variables. However, empirically, ‘continuous’ variables are also always observed as discrete categories, defined by the precision of the measuring instrument. Thus, log linear modelling techniques can be applied to such data, at least if
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Abstract Categorical data models have traditionally been used to study nominal, and ordinal, response variables. However, empirically, ‘continuous’ variables are also always observed as discrete categories, defined by the precision of the measuring instrument. Thus, log linear modelling techniques can be applied to such data, at least if
openaire +1 more source