Study programme competencies |
Code
|
Study programme competences / results
|
A3 |
CE3 - Capacidade para a análise de datos e a comprensión, modelado e resolución de problemas en contextos de aleatoriedade. |
A17 |
CE17 - Capacidade para a construción, validación e aplicación dun modelo estocástico dun sistema real a partir dos datos observados e a análise crítica dos resultados obtidos. |
A20 |
CE20 - Coñecemento das ferramentas informáticas no campo da análise dos datos e modelización estatística, e capacidade para seleccionar as máis adecuadas para a resolución de problemas. |
B2 |
CB2 - Que os estudantes saiban aplicar os seus coñecementos ao seu traballo ou vocación dunha forma profesional e posúan as competencias que adoitan demostrarse por medio da elaboración e defensa de argumentos e a resolución de problemas dentro da súa área de estudo |
B3 |
CB3 - Que os estudantes teñan a capacidade de reunir e interpretar datos relevantes (normalmente dentro da súa área de estudo) para emitir xuízos que inclúan unha reflexión sobre temas relevantes de índole social, científica ou ética |
B4 |
CB4 - Que os estudantes poidan transmitir información, ideas, problemas e solucións a un público tanto especializado como non especializado |
B6 |
CG1 - Ser capaz de buscar e seleccionar a información útil necesaria para resolver problemas complexos, manexando con soltura as fontes bibliográficas do campo. |
B7 |
CG2 - Elaborar adecuadamente e con certa orixinalidade composicións escritas ou argumentos motivados, redactar plans, proxectos de traballo, artigos científicos e formular hipóteses razoables. |
B8 |
CG3 - Ser capaz de manter e estender formulacións teóricas fundadas para permitir a introdución e explotación de tecnoloxías novas e avanzadas no campo. |
B9 |
CG4 - Capacidade para abordar con éxito todas as etapas dun proxecto de datos: exploración previa dos datos, preprocesado, análise, visualización e comunicación de resultados. |
B10 |
CG5 - Ser capaz de traballar en equipo, especialmente de carácter multidisciplinar, e ser hábiles na xestión do tempo, persoas e toma de decisións. |
C1 |
CT1 - Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida. |
C4 |
CT4 - Valorar a importancia que ten a investigación, a innovación e o desenvolvemento tecnolóxico no avance socioeconómico e cultural da sociedade. |
Learning aims |
Learning outcomes |
Study programme competences / results |
Know and understand the basics of missing data |
A3 A20
|
B6
|
C1 C4
|
To know the main techniques to analyse problems with missing data |
A3 A17 A20
|
B3 B4 B9
|
C1
|
To know the main techniques to analyse functional data |
A3 A17 A20
|
B3 B4 B9
|
C1
|
To know the main techniques to analyse censored data |
A3 A17 A20
|
B3 B4 B9
|
C1
|
To know the main techniques to analyse problems with biased data |
A3 A17 A20
|
B3 B4 B9
|
C1
|
To be able to apply different techniques for missing data, functional data, censored data and biased data to a real or a simulated dataset |
A20
|
B2 B3 B4 B9
|
C1
|
To be able to interpret the results and to know the limitations of the different methods |
A3
|
B6 B7 B8 B10
|
C1 C4
|
Contents |
Topic |
Sub-topic |
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 |
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 |
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 |
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 |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Oral presentation |
A3 B2 B3 B4 C4 |
21 |
31.5 |
52.5 |
ICT practicals |
A17 A20 A3 B2 B3 B4 B6 B7 B8 B9 B10 C1 |
7 |
24.5 |
31.5 |
Supervised projects |
A17 A20 A3 B2 B3 B4 B6 B7 B9 B10 C1 |
3.5 |
15.75 |
19.25 |
Problem solving |
A17 B2 B7 B8 B10 |
7 |
28 |
35 |
Mixed objective/subjective test |
A20 A3 B2 B3 B4 B8 C1 |
1.5 |
3 |
4.5 |
Mixed objective/subjective test |
A20 A3 B2 B3 B4 B8 C1 |
1.5 |
3.75 |
5.25 |
|
Personalized attention |
|
2 |
0 |
2 |
|
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Oral presentation |
Presentation using the computer |
ICT practicals |
Statistical data analysis using R |
Supervised projects |
Statistical analyses of some databases applying the studied methodologies |
Problem solving |
Deciding statistical tools and strategies for problem solving with missing data, functional data, censored data or biased data |
Mixed objective/subjective test |
Test related to concepts and/or practical exercises with R (in the middle of the semester) |
Mixed objective/subjective test |
Test related to concepts and/or practical exercises with R (official exam in January) |
Personalized attention |
Methodologies
|
Problem solving |
ICT practicals |
Supervised projects |
|
Description |
Attendance and participation in the theoretical lessons
Practical cases of study using R
Problem solving using R
Test related to theoretical and/or practical questions
|
|
Assessment |
Methodologies
|
Competencies / Results |
Description
|
Qualification
|
Mixed objective/subjective test |
A20 A3 B2 B3 B4 B8 C1 |
Test related to concepts and/or practical exercises with R (official exam in January) |
40 |
Supervised projects |
A17 A20 A3 B2 B3 B4 B6 B7 B9 B10 C1 |
Practical and research projects |
40 |
Mixed objective/subjective test |
A20 A3 B2 B3 B4 B8 C1 |
Test related to concepts and/or practical exercises with R (in the middle of the semester) |
20 |
|
Assessment comments |
The assessment scoring will be the following: • Two or three practical and research works: 4 points. • Concept/practical test related to Topics 1-2: 2 points. It will take place in the middle of the quadrimester. Students will avoid this test in the official exam if they obtain, at least, a score of 3.5 out of 10 in this first exam, unless they want to get a higher score (in this case, the score will be the one obtained in the official exam). Moreover, if students do not attend the first exam or if they obtain a score lower than 3.5 out of 10, then they will be evaluated (the day of the official exam) of this. • Concept/practical test related to Topics 3 - 6: 4 points. It will take place the date of the official exam. In order to pass the subject it is necessary to obtain a score of at least 3.5 out of 10 in this test. To pass the subject it is necessary to obtain a score of at least 5 out of 10 overall. On the second opportunity (July), students must attend the exams in which they obtained a lower score than 3.5 out of 10 in January tests. If they want to get a higher score, then the final score will be the one obtained in July.
Only students that didn't take any test will be qualified as NON ATTENDANT in the first opportunity (January-February). In July (2nd opportunity) only students that didn't take the final exam will be qualified as NON ATTENDANT. If a student wants to take a test in a specific official language (Spanish or Galician), he/she must inform the professor at least 1 week in advance. Fraud in tests or evaluation activities will directly imply the failure grade "0" in the subject in the corresponding call, thus invalidating any grade obtained in all the evaluation activities for the extraordinary call.
|
Sources of information |
Basic
|
Hosmer D. W., Lemeshow S., May S. (2008). Applied survival analysis: regression modeling of time-to-event data. Wiley-Interscience
Qin J. (2017). Biased sampling, over-identified parameter problems and beyond (Vol. 5). Springer
Ramsay J. O., Silverman B. W. (2005). Functional Data Analysis. 2nd Edition. Springer
Ferraty F., Vieu P. (2006). Nonparametric functional data analysis : theory and practice. Springer
Cox D. R. (2005). Some sampling problems in technology. . Selected Statistical Papers of Sir David Cox
Little R. J., Rubin D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons
Lee E. T., Wang J. W. (2013). Statistical Methods for Survival Data Analysis. 4th Edition. Wiley |
|
Complementary
|
Therneau T. (2021). A Package for Survival Analysis in R. CRAN
Van Buuren, S. (2018). Flexible imputation of missing data. CRC Press
Therneau T. M., Grambsch P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer
Febrero-Bande M, Oviedo de la Fuente M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1–28 |
|
Recommendations |
Subjects that it is recommended to have taken before |
Statistical Analysis of Dependent Data/614G02022 | Regression Models/614G02012 | Statistical Modeling of High Dimensional Data/614G02013 | Statistical Inference/614G02007 | Probability and Basic Statistics/614G02003 |
|
Subjects that are recommended to be taken simultaneously |
Spatiotemporal Data Representation and Management/614G02035 | Simulation and Resampling Techniques/614G02036 |
|
Subjects that continue the syllabus |
Omics Data Management and Modeling /614G02042 |
|
Other comments |
As stated in the different applicable regulations for university teaching, the gender perspective must be incorporated in this course (non-sexist language will be used, bibliography of authors of both genders will be used, intervention in class of both male and female students will be encouraged, etc.) Work will be done to identify and modify prejudices and sexist attitudes. The environment will be influenced to modify these prejudices and attitudes, to promote values of respect and equality. Situations of discrimination based on gender must be detected. Actions and measures to correct them will be proposed.
|
|