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| Statistica Descrittiva Bayesiana× | Statistiche Descrittive Robuste× | |
|---|---|---|
| Campo | Statistica | Statistica |
| Famiglia | Hypothesis test | Hypothesis test |
| Anno di origine≠ | 1763/1812 | 1960s–1970s |
| Ideatore≠ | Thomas Bayes / Pierre-Simon Laplace | John W. Tukey, Peter J. Huber, Frank Hampel |
| Tipo≠ | Bayesian parameter estimation | Resistant summary measures |
| Fonte seminale≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley. ISBN: 978-0201076165 |
| Alias | Bayesian summaries, posterior descriptives, Bayesian parameter estimation, credible-interval summaries | resistant statistics, outlier-resistant summary statistics, robust summary measures, robust location and scale estimation |
| Correlati | 5 | 5 |
| Sintesi≠ | Bayesian descriptive statistics summarizes data by combining observed information with prior knowledge through Bayes' theorem, yielding posterior distributions over parameters such as the mean and variance. Instead of point estimates and p-values, results are expressed as posterior means, medians, and credible intervals that carry a direct probability interpretation. | Robust descriptive statistics summarize the location, spread, and shape of a dataset using measures that remain meaningful even when a fraction of the data contains outliers or severe departures from normality. Core tools include the median, trimmed mean, interquartile range (IQR), and median absolute deviation (MAD), all of which are resistant to contamination that would distort the classic mean and standard deviation. |
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