Teaching GuideTerm Faculty of Computer Science |
Grao en Ciencia e Enxeñaría de Datos |
Subjects |
Statistical Analysis of Complex Data |
Contents |
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Identifying Data | 2022/23 | |||||||||||||
Subject | Statistical Analysis of Complex Data | Code | 614G02031 | |||||||||||
Study programme |
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Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Graduate | 1st four-month period |
Fourth | Optional | 6 | ||||||||||
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Topic | Sub-topic |
Introduction to missing data | Challenges and problems with missing data Missing data mechanisms: missing at random (MAR) and missing completely at random (MCAR) The consequences of discarding missing data |
Imputation methods | Mean imputation Single imputation methods Maximum likelihood multiple imputation under MAR Expectation{Maximization (EM) algorithm Multiple imputation methods under MAR |
Introduction to functional data | Motivation and examples Functional data registration and smoothing Metrics and semimetrics for functional data Representing functional data: basis expansions |
Functional data analysis | Estimation of mean and covariance operator On the concept of depth for functional data: functional anomaly detection Functional principal component analysis Functional linear models |
Censored data | Missing data and censoring The consequences of discarding censored data Parametric estimation for censored data Nonparametric estimation for censored data: the Kaplan-Meier estimator Cox model: conditional survival |
Biased data | Selection bias: length, time and size The consequences of disregarding bias Mean and variance estimation for biased data Likelihood principle for biased data Situations with unspecified bias function |
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