Titlebook: Statistical Distributions in Scientific Work; Volume 5 — Inferenti Charles Taillie,Ganapati P. Patil,Bruno A. Baldess Book 1981 D. Reidel P

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Explicit Accurate Approximations for Fitting the Parameters of LUThe Tadikamalla-Johnson unlimited system based on the Logistic density may be fitted through the equivalence of the skewness (✓β.) and kurtosis (β.) in the model and distribution approximated. One-shot accurate approximations compact enough for small calculators and covering an extensive domain are described.

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Testing Outliers in Multivariate Datass powerful under scale changes (model B; Barnett and Lewis). Like R, g is not sensitive to changes in correlations (orientation). The statistic g can be used (under models A or B) for testing outliers in samples from any multivariate distribution whose marginal distributions are of the type (l/σ)f((x-μ)/σ).

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Conditional Maximum Likelihood Estimation in Gaussian Mixtureslo study are reported for the univariate case and these show that the procedures provide reasonable estimators for small sample sizes. The methods are then extended to mixtures of multivariate gaussian populations.

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An Asymptotically Distribution-Free Goodness-of-Fit Test for Families of Statistical Distributions Dgeneral conditions. The null distribution of the test statistic does not depend on the nuisance parameters and is asymptotically distribution-free. The paper gives a short table of its critical values and a table of its power against some alternatives.

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A Survey of Estimating Distributional Parameters and Sample Sizes from Truncated Samplesmber n of observed X’s is a binomial (N,P) variate, where P = 1 — P(X in R). Based on n and the values of the observed X’s, we want to estimate N and θ. Conditional and unconditional maximum likelihood estimators and modifications of these will be discussed. Asymptotic results of both first and seco

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