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Identified Set (identified + set)

Distribution by Scientific Domains

Chemistry	50%

Selected Abstracts

Estimation and Confidence Regions for Parameter Sets in Econometric Models,

ECONOMETRICA, Issue 5 2007
Victor Chernozhukov
This paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities. Applications of this framework include the analysis of game-theoretic models, revealed preference restrictions, regressions with missing and corrupted data, auction models, structural quantile regressions, and asset pricing models. Specifically, we provide estimators and confidence regions for the set of minimizers ,I of an econometric criterion function Q(,). In applications, the criterion function embodies testable restrictions on economic models. A parameter value ,that describes an economic model satisfies these restrictions if Q(,) attains its minimum at this value. Interest therefore focuses on the set of minimizers, called the identified set. We use the inversion of the sample analog, Qn(,), of the population criterion, Q(,), to construct estimators and confidence regions for the identified set, and develop consistency, rates of convergence, and inference results for these estimators and regions. To derive these results, we develop methods for analyzing the asymptotic properties of sample criterion functions under set identification. [source]

Enantioselective Proteins: Selection, Binding Studies and Molecular Modeling of Antibodies with Affinity towards Hydrophobic BINOL Derivatives

CHEMBIOCHEM, Issue 16 2007
Brian Schou Rasmussen Dr.
Abstract In this paper, the initial steps towards the design of novel artificial metalloenzymes that exploit proteins as a second coordination sphere for traditional metal,ligand catalysis are described. Phage display was employed to select and study antibody fragments capable of recognizing hydrophobic BINOL derivatives designed to mimic BINAP, a widely used ligand in asymmetric metal-catalyzed reactions. The binding affinities of the selected antibodies towards a series of haptens were evaluated by using ELISA assays. A homology model of one of the most selective antibodies was constructed, and a computer-assisted ligand-docking study was carried out to elucidate the binding of the hapten. It was shown that, due to the hydrophobic nature of the haptens, a higher level of theoretical treatment was required to identify the correct binding modes. A small selection of the antibodies was found to discriminate between enantiomers and small structural modifications of the BINOL derivatives. The selectivities arise from hydrophobic interactions, and we propose that the identified set of antibodies provides a foundation for a novel route to artificial metalloenzymes. [source]

Bounds on Parameters in Panel Dynamic Discrete Choice Models

ECONOMETRICA, Issue 3 2006
Bo E. Honoré
Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using these insights, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases, and the failure of point identification may, therefore, be of little practical importance in those cases. Although the emphasis is on identification, our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets. [source]

TICL , a web tool for network-based interpretation of compound lists inferred by high-throughput metabolomics

FEBS JOURNAL, Issue 7 2009
Alexey V. Antonov
High-throughput metabolomics is a dynamically developing technology that enables the mass separation of complex mixtures at very high resolution. Metabolic profiling has begun to be widely used in clinical research to study the molecular mechanisms of complex cell disorders. Similar to transcriptomics, which is capable of detecting genes at differential states, metabolomics is able to deliver a list of compounds differentially present between explored cell physiological conditions. The bioinformatics challenge lies in a statistically valid interpretation of the functional context for identified sets of metabolites. Here, we present TICL, a web tool for the automatic interpretation of lists of compounds. The major advance of TICL is that it not only provides a model of possible compound transformations related to the input list, but also implements a robust statistical framework to estimate the significance of the inferred model. The TICL web tool is freely accessible at http://mips.helmholtz-muenchen.de/proj/cmp. [source]