Textbook The course will mainly follow A. W. van der Vaart (1998) Asymptotic Statistics. Cambridge University Press, Cambridge, UK.
Structure Lectures and exercises Assessment Hand-in exercises.
Pre-requisites Statistical Inference I (7.5 hp), PhD level, or equivalent.
Course plan Selected chapters from van der Vaart (1998) will be covered during the course. The course will be held during June (weeks 23, 24, 25) and August (weeks 33 and 34).
Submitted by jolanta.pielasz... on February 6, 2021 - 09:58
Course plan
The course is about optimal experimental design – planning of experiments. The basic idea of design optimization (best estimation of unknown model parameters, information or moment matrix, etc.) and commonly used design criteria in linear models are the main parts of the course. Besides optimal designs in classical linear models, optimal designs for estimation and prediction of fixed and random effects in particular mixed models will be discussed.
Submitted by Frank Miller on January 8, 2021 - 12:55
Based on the topics discussed in first part of the course, we continue with deepening the theoretical basis for stochastic optimisation algorithms. Specifically, we discuss theory around Stochastic Gradient Ascent (including momentum and adaptive step sizes), Simulated Annealing, and Particle Swarm Optimisation. Theoretical results on convergence and speed will be discussed.
Submitted by xavierdeluna on October 5, 2020 - 13:20
This course will be run on-line via zoom, except for the final exam which is onsite (but local examination might be arranged for PhD students at other universities).
Submitted by larsronn on September 18, 2020 - 08:06
Upon completion of the course, the doctoral student will be able to: explain the basic theoretical foundation and practical use of Structural Equation Models (SEM), apply SEM on real problems, interpret and present the results, estimate structural equation models with maximum likelihood and least squares and be able to evaluate the results.
A Ph.D. course in modern statistical inference theory for students in statistics, mathematical statistics, and related areas. Two onsite meetings (Lund and Stockholm) and three online meetings. The details about the course can be found at https://krys.neocities.org/Teaching/StatInf/PhD_stat_inf.html.
We will discuss algorithms to compute minima or maxima which are frequently needed in statistics and machine learning. Topics of lectures on four occasions: gradient based algorithms, stochastic gradient based algorithms, gradient free algorithms (e.g. particle swarm optimisation), handling of restrictions during optimisation. Course homepage: http://www.adoptdesign.de/optimisation1.html
All lectures are online via Zoom. The lecturer is Prof. Dr. Claudia Czado (https://www.professoren.tum.de/en/czado-claudia/) who also wrote the course book. Info regarding the course is available in the attached course plan. The course will take place in the last week of august (detailed schedule is attached). Any questions contact Kristofer Månsson (kristofer.mansson@ju.se).
Due to the outbreak of the coronavirus all lectures will be online via Zoom.The lecturer is Dr. Abdul Aziz Ali. Info regarding the course is available in the attached course plan. The course will start in the midle of may. Any questions contact Kristofer Månsson (kristofer.mansson@ju.se).