Teaching GuideTerm Faculty of Computer Science |
Mestrado Universitario en Técnicas Estadísticas (Plan 2019) |
Subjects |
Nonparametric Methods |
Contents |
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Identifying Data | 2020/21 | |||||||||||||
Subject | Nonparametric Methods | Code | 614493111 | |||||||||||
Study programme |
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Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Official Master's Degree | 1st four-month period |
First | Obligatory | 5 | ||||||||||
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Topic | Sub-topic |
Nonparametric distribution estimation |
The empirical distribution. Properties. Moments and quantiles estimation. |
Classical one-sample nonparametric tests. | Goodness-of-fit tests: Kolmogorov-Smirnov test. Normality analysis: Q-Q plot, Lilliefors test, Shapiro-Wilk test, transformations for normality. One-sample location tests: sign test, Wilcoxon signed-rank test. |
Two-sample tests. |
Two-sample comparison: Kolmogorv-Smirnov test for two-samples, Mann-Whitney-Wilcoxon test. Extensions for three or more samples: Kruskal-Wallis test, Friedman test. |
Tests based on contingency tables. | Contingency tables analysis. Chi-squared tests for goodness-of-fit, homogeneity and independence on contingency tables. |
Smoothing methods: nonparametric density estimation. | The histogram. Kernel density estimation. Assessment of density estimators. Smoothing parameter selectors in kernel density estimation: cross-validation and plug-in approaches. Multivariate kernel density estimation. |
Nonparametric regression estimation. | Kernel regression. Local polynomial regression. k-nearest neighbor regression. Smoothing parameter selectors in kernel regression estimation: cross-validation and plug-in approaches. Loess algorithm. Spline regression: a brief introduction. |
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