| Date | Talk |
|---|---|
| 5 May 2010 and 19 May 2010 | Thomas Augustin: Imprecise Measurement Error Models and Partial Identification — Towards a Unified Approach for Non-Idealized Data (1st talk, 2nd talk) The talk ventilates some first steps towards a generalized, unified handling of deficient, nay non-idealized, data. The ideas are based on a more general understanding of measurement error models, relying on possibly imprecise error and sampling models. This modelling comprises common deficient data models, including classical and non-classical measurement error, coarsened and missing data, as well as neighbourhood models used in robust statistics. Estimation is based on an eclectic combination of concepts from Manski's theory of partial identification and from the theory of imprecise probabilities. (Not only) as a preparation, the first part of the talk discusses measurement error modelling with precise probabilities. After a brief introduction into the background, I consider one of the most general methods to correct for classical measurement error, namely Nakamura's method of corrected score functions. It is shown how this method to construct unbiased estimating functions under measurement error can be extended to deal with other types of error models, in particular with deficient dependent variables and with the so-called Berkson error. The second part of the talk extends consideration to imprecise probabilities, relaxing the rather rigorous assumptions underlying all the common measurement error models. The concept of partial identification is extended to estimating equations by considering sets of potentially unbiased estimating functions. Some properties of the corresponding set-valued parameter estimators are discussed, including their consistency (in an appropriately generalized sense). Finally, the relation to previous work in the literature on partial identification in linear models is made explicit. |
| 12 May 2010 | Gero Walter: "Strong happiness" and other properties of certain imprecise probability models when treating samples sequentially Generalized iLUCK-models, a model introduced by Walter and Augustin (2009) as an imprecise probability generalization of conjugate Bayesian inference, have the advantage of an adaptive reaction to prior-data conflict. Whereas standard conjugate Bayesian inference is not necessarily sensitive to conflicts between prior and data, generalized iLUCK-models lead to much more cautious inferences if prior and data are in conflict. In this talk, the case of data trickling in as separate portions of observations is investigated, and a number of ideas related to sequential updating of the prior are presented, exploiting the sensitivity to prior-data conflict that generalized iLUCK-models offer. "Strong happiness" is a concept using these ideas for a simple sample size calculation, guaranteeing a certain precision with the possibility of prior-data conflict factored in. |
| 2 June 2010 | Marco Cattaneo: Independence and Combination of Belief Functions In belief functions theory, information about an uncertain value is described by a random set, and not by a random variable. We shall discuss some ideas about the interpretation of belief functions and the fusion of dependent information. |
| 28 June 2010
(Monday) |
Christina Schneider: Randomness Does Not Exist Die Kenner werden feststellen, dass dieser Titel sich an de Finettis Diktum "Probability does not exist" anlehnt. Während de Finetti daraus den Schluss zieht, Wahrscheinlichkeit sei subjektivistisch zu interpretieren, wird dieser Weg nicht beschritten werden. Zunächst wird, im Rahmen einer "objektivistischen" Inferenzschule zu dieser These hingeführt — hierzu sind einige wissenschaftstheoretische Überlegungen nötig — und dann werden einige Konsequenzen dieser Hinführung gezogen. Die wichtigste Konsequenz ist, dem "Wahrheitsanspruch" von Wahrscheinlichkeiten bzw. Wahrscheinlichkeitsaussagen zu entsagen. Die positive Konsequenz ist pragmatischer Natur: Statistische Inferenz als Inference to the Best (Idealized) Description aufzufassen. |
| 30 June 2010 | Andrea Wiencierz: The course of well-being over the life span — Restricted Likelihood Ratio Testing (RLRT) in the presence of correlated errors Tests for zero variance components in general form linear mixed models (LMMs) have been established for different cases where the errors are assumed to be independent and identically distributed (i.i.d.). These tests can be applied to many interesting questions in practice. They allow, for example, to test if a relation between two variables is significantly different from a polynomial of a given degree. However, in many real applications the independence of the errors is not given. For example in economic applications the errors are often positively autocorrelated. In the case of the ordinary linear model, there is a simple transformation technique to take the correlation into account, known to econometricians as Generalized Least Squares (GLS) transformation. Motivated by an economic study about the course of subjective well-being over the life span, the transformation technique is adapted to the case of general form LMMs, and it is investigated if this transformation technique can be used for expanding the application areas of the established tests for zero variance components to the case of correlated errors. |
| 7 July 2010 | Hansjörg Baurecht: Detecting Signals in Genomewide Association Studies Genomewide data which are collected to detect statistical associations between SNPs and complex traits are usually analyzed by univariate testing of each SNP with the trait. To account for the large number of significance tests carried out, a very stringent p-value is used. This reduces occurrence of false positives, but it may cause many real associations to be missed. I will discuss an idea to incorporate the consideration of a region of SNPs where each single SNP does not pass the detection threshold. But by aggregating them so far undetected associated regions might be discovered. Therefore I adopted the idea of kernel smoothing to calculate a combined statistic incorporating the genetic distance and the linkage disequilibrium. |
| 19 July 2010
(Monday) |
Julia Kopf: Reflecting methods from machine learning with respect to their application in social science, psychology and statistics Some ideas about the application and interpretation of methods from machine learning like recursive partitioning or association rules are presented. The main focus of the talk lies on the statistical validation of Ockham's Razor using model-based recursive partitioning. |