A bayesian nonparametric estimator based on left censored by Walker S., Muliere P. PDF

By Walker S., Muliere P.

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Let the number of samples be m and the total number of values n. First, we calculate the squared difference between each sample mean and the total mean of the pooled dataset (all samples combined), and multiply each of these with the corresponding sample size. 24) The BgSS divided by m − 1 (degrees of freedom) gives the betweengroups mean square (BgMs). We now have a measure of between-groups variance. Next, we compute the squared difference between each value and the mean of the sample it belongs to.

Both parametric and non-parametric tests will be treated in this chapter. 3 Shapiro–Wilk test for normal distribution Purpose To test whether a given sample has been taken from a population with normal distribution. This test may be carried out prior to the use of methods that assume normal distribution. Data required A sample of measured data. 16). A useful graphic method is the normal probability plot (appendix A). However, according to D’Agostino and Stevens (1986, p. 406), the best overall performer for both small and large samples is perhaps the Shapiro– Wilk test (Shapiro & Wilk 1965, Royston 1982).

This finally gives a p value for the equality of all group means. 10 Kruskal–Wallis test Purpose To test whether several univariate samples are taken from populations with equal medians. Data required Two or more independent samples with similar distributions, each containing a number of univariate, continuous (measured) or ordinal values. The test is nonparametric, so a normal distribution is not assumed. Description The Kruskal–Wallis test is the non-parametric alternative to ANOVA, just as Mann–Whitney’s U is the non-parametric alternative to the t test.

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A bayesian nonparametric estimator based on left censored data by Walker S., Muliere P.


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