Research Seminar
Time/Place: every Friday at 3.15 pm in the Main Building of ETH, HG G 19.1
Spring Semester 2012
| Date |
Speaker |
Title |
Time |
Location |
| 16-mar-2012 (fri) |
Sander Greenland
|
Integrating Bayesian and frequentist statistics, or: Seeing both sides of the same biased coin.
|
15:15-16:30 |
HG G 19.1 |
| Abstract: |
Outlines of a bayes‐non‐Bayes compromise or fusion have been emerging for decades.
Nonetheless, basic teaching remains mired in conventional frequentist methods that are
misunderstood and misrepresented by most users (including many statisticians) and that are
highly misleading outside of ideal experimental conditions. Thus it is essential to revolutionize
how we introduce elementary statistical inference in health and social science, by providing
Bayesian concepts and methods in tandem with frequentist concepts and methods. Contrary to
prevalent beliefs, basic Bayesian methods require no new computational formulas or software
beyond familiar frequentist ones; they do not even require Bayes theorem. Those methods can
help reveal untenably strong assumptions hidden in conventional methods, and allow relaxation of
those assumptions into a more reasonable form.
Background cite: Greenland, S. (2009). Relaxation penalties and priors for plausible modeling of
nonidentified bias sources. Statistical Science, 24, 195‐210 |
| Speakers: |
Sander Greenland
(University of California, Loa Angeles (UCLA))
|
|
| 20-mar-2012 (tue) |
Bin Yu
|
tba
|
15:15-16:30 |
HG G 19.1 |
| Abstract: |
tba |
| Speakers: |
Bin Yu
(University of California, Berkeley)
|
|
| 23-mar-2012 (fri) |
Marc Hallin
|
One-Sided Representations of Generalized Dynamic Factor Models
|
15:15-16:30 |
HG G 19.1 |
| Abstract: |
Factor model methods recently have become extremely popular in the theory and practice
of large panels of time series data. Those methods rely on various models which all are particular
cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and
Reichlin (2000). In that paper, however, estimation relies on Brillinger's concept of dynamic principal
components, which produces filters that are in general two-sided and therefore yield poor performances
at the end of the observation period and hardly can be used for prediction purposes. In this talk, we
show how to remedy this problem, and how, based on recent results on singular stationary processes
with rational spectra, one-sided estimators can be constructed for the parameters and the common
shocks in the GDFM. Consistency is obtained, along with rates. An empirical example, based on US
macroeconomic time series, compares estimates based on our model with those based on the usual
static-representation restriction, and provides convincing evidence that the assumptions underlying
the latter are not supported by the data.
|
| Speakers: |
Marc Hallin
(Universität Brüssel)
|
|
| 30-mar-2012 (fri) |
Claudia Czado and Alexander Bauer
|
Model selection for pair-copula constructions of regular vine and non-Gaussian DAG models
|
15:15-16:30 |
HG G 19.1 |
| Abstract: |
Pair-copula constructions (PCCs) allow to build very flexible multivariate statistical models based on a graphical representation called a regular vine (Kurowicka and Cooke, 2006) as well as models represented by directed acyclic graphs (DAGs). PCCs are very useful for modeling multivariate data in economics and finance, since they can capture non-symmetric and different tail dependences for different pairs of variables separately. Vine models are characterized by a sequence of linked trees called a vine-tree structure, bivariate copula families and families of marginal distributions. Two often studied subclasses are C- and D-vines. The multivariate normal and t distribution families are special cases. Moreover, PCCs can be used to construct non-Gaussian DAG models. First, research was focused on the development of efficient estimation methods. For regular vine models see for example Aas et. al. (2009) for likelihood based and Min and Czado (2010) for Bayesian estimation methods. For non-Gaussian DAGs model formulation and estimation methods are considered in Bauer et. al. (2012). Since the class of regular vine models is very large, model selection is vital. Dissmann et. al. (2011) provide a fast selection method in which trees are sampled sequentially using algorithms for weighted graphs. Bayesian alternatives are available. For non-Gaussian DAGs the model selection involves also a data-based selection of the DAG. We provide an alternative approach to the PC algorithm (Spirtes et. al., 2001) based on regular vines to allow for the detection of non-Gaussian dependency structures and compare its performance to the benchmark PC algorithm based on an independence test for zero partial correlation. We will discuss these PCC models and the associated selection methods as well illustrate them in an application to daily stock returns. |
| Speakers: |
Claudia Czado and Alexander Bauer
(Technische Universität, München)
|
|
| 20-apr-2012 (fri) |
Sen Bodhisattva
|
tba
|
15:15-16:30 |
HG G 19.1 |
| Abstract: |
tba |
| Speakers: |
Sen Bodhisattva
(University of Cambridge, Cambridge UK)
|
|
| 1-jun-2012 (fri) |
Gabor Lugosi
|
tba
|
15:15-16:30 |
HG G 19.1 |
| Abstract: |
tba |
| Speakers: |
Gabor Lugosi
(Universitat Pompeu Fabra, Barcelona)
|
|
Further information: sekretariat@stat.math.ethz.ch
