Future events

TUESDAY Statistics Seminar: Riccardo De Bin

Tuesday, February 28, 2017 - 14:15

Speaker: Riccardo De Bin (Department of Mathematics,UiO)

Title: Strategies to handle mandatory covariates using model- and likelihood-based boosting

Abstract:  Among the iterative methods exploited during recent years in statistical practice, particular attention has been focused on boosting. Originally developed in the machine learning community to handle classification problems, boosting has been successfully translated into the statistical field and extended to many statistical problems, including regression and survival analysis. In a parametric framework, the basic idea of boosting is to provide estimates of the parameters by updating their values iteratively: at each step, a weak estimator is fitted on a modified version of the data, with the goal of minimizing a loss function. Thanks to its resistance to overfitting, boosting is particularly useful in the construction of prediction models. Its iterative nature, moreover, allows straightforward adaptations to cope with high-dimensional data. In this talk, we first review and contrast two well-known boosting techniques, model-based boosting and likelihood-based boosting. We note that in the simple linear regression case they lead to the same results, provided there is a specific choice for their tuning parameters. This is not the case for more complex situations. As an example, we show the differences in survival analysis under the proportional hazards assumption. As a main contribution of the talk, we analyze strategies to include mandatory variables, i.e. those variables that for some reasons must enter in the final model, in a statistical model using the two boosting techniques. In particular, we examine solutions currently only considered for one and explore the possibility of extending them to the other. We show the importance of a good handling of mandatory variables in a real data example.

Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

Wednesday Lunch: Karianne de Bruin

Wednesday, March 1, 2017 - 12:00

Speaker: Karianne de Bruin, senior researcher at Cicero, and researcher at the CALM team at Wageningen Environmental Research, Netherlands.

Title: Adaption decision-making under uncertainty

Abstract: The presentation will highlight the theoretical background of decision-making under uncertainty in the context of adapting to climate change and will discuss how this fits into the context of 'climate services', services which aim to facilitate the production, translation and tailoring of climate information to build long term resilience.

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Spiseriet, Norwegian Computing Center

THURSDAY Biostatistical Seminar: Hein Putter

Thursday, March 9, 2017 - 14:15

Speaker: Hein Putter, Proffesor, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: TBA

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)

Wednesday Lunch: TBA

Wednesday, March 15, 2017 - 12:00

Speaker: TBA

The lunch starts at 12:00, and the talk will start around 12:2

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

THURSDAY Biostatistical Seminar: Harald Binder

Thursday, March 16, 2017 - 14:15

Speaker: Harald Binder, Professor, Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University Mainz, Germany.

Title: Two uses of stagewise regression: from landmarking in cancer patients to deep learning for SNPs

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: Regularized techniques allow to estimate the parameters of a regression model and to perform variable selection even when the number of covariates is larger than the number of observations.  Penalized likelihood approaches are the most prominent way for performing such regularization, e.g. the Lasso for variable selection. Stagewise regression techniques, also known as componentwise boosting, provide an alternative to the latter. Starting from estimates all equal to zero, one parameter is updated in each of a potentially large number of steps. Typically, this does not stop at the maximum of a global criterion, such as a penalized log-likelihood, which makes such approaches difficult to treat analytically might be seen as a disadvantage. Yet, there are two distinct advantages: optimization in settings where no (overall) likelihood is a available, and speed. I will show two applications, where these advantages play out. In a dynamic prediction application with data from hepatocellular carcinoma patients, effects of patient characteristics on the cumulative incidence of death (in presence of competing risks) are investigated using pseudo-value regression models at different landmarks. Stagewise regression allows to couple variable selection between landmarks without enforcing similarity between parameter estimates. As a result the approach selects a set of variables that is relevant for all landmarks. In a second application, the aim is to search for potentially complex patterns in molecular data, namely single nucleotide polymorphisms (SNPs), which might be relevant for patient prognosis. The modeling of patterns is performed by a deep learning approach, specifically deep Boltzmann machines, which cannot be applied in a straightforward manner if the number of SNPs is larger than the number of observations. Thus, stagewise regression is used to obtain a rather crude (implicit) estimate of the joint distribution of SNPs, which forms the basis for partitioned training of deep Boltzmann machines. The resulting SNP patterns are seen to be relevant with respect to clinical outcomes.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)

Wednesday Lunch: Steffen Sjursen

Wednesday, March 29, 2017 - 12:00

Speaker: Steffen Sjursen, Senior analyst, group risk modelling, DNB. PhD UiO: Stochastic Optimal Control and time changed Lévy noises.

Title: Calculating probability of default when available data varies greatly between different customers.

Abstract: TBA

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Spiseriet, Norwegian Computing Center

THURSDAY Biostatistical Seminar: Georg Heinze

Thursday, March 30, 2017 - 14:15

Speaker: Georg Heinze, Professor, Center for Medical Statistics, Informatics, and Intelligent Systems (Section for Clinical Biometrics), Medical University of Vienna, Austria

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: TBA

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)

Wednesday Lunch: TBA

Wednesday, April 19, 2017 - 12:00

Speaker: TBA

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

Wednesday Lunch: TBA

Wednesday, May 3, 2017 - 12:00

Speaker: TBA

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Spiseriet, Norwegian Computing Center

Wednesday Lunch: TBA

Wednesday, May 24, 2017 - 12:00

Speaker: TBA

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics