Estimation of probability density function with applications. Randomization tests and Monte Carlo.
Stochastic simulation: generating uniform random deviates, inversion method, rejection sampling, variance reduction techniques. MCMC algorithms: Metropolis-Hastings algorithm. Resampling methods: Jackknife, bootstrap, cross-validation. EM algorithm. Applications of these methods using the R program.
- Teacher: Δημήτριος Φουσκάκης
Συνδιδασκαλία: 3641
ECTS : 5
Language : el
Learning Outcomes : Upon successful completion of the course, the student will be able to:
• Understand the usefulness and the mathematically grounded methodology of the computational statistical methods taught, as well as the types of problems to which they can be applied.
• Fully comprehend the significance and necessity of these methods in various statistical data-analysis problems.
• Explain in simple terms the results obtained after implementing these techniques.
• Compute and implement, with the help of the R programming language, the methods taught, using ready-made packages or by creating their own functions.
• Generalize and combine the methods they have learned.
• Be guided in a structured and comprehensible way to internalize the theory and practices applied to data-analysis problems using modern methods, with the aim of informed decision-making.