Regularization and Model Selection with Categorial Predictors and Effect Modifiers in Generalized Linear Models

We consider varying-coefficient models with categorial effect modifiers in the framework of generalized linear models. We distinguish between nominal and ordinal effect modifiers, and propose adequate Lasso-type regularization techniques that allow for (1) selection of relevant covariates, and (2) identification of coefficient functions that are actually varying with the level of a potentially effect modifying factor. We investigate the estimators’ large sample properties, and show in simulation studies that the proposed approaches perform very well for finite samples, too. Furthermore, the presented methods are compared with alternative procedures, and applied to real-world medical data

Regularization and Model Selection with Categorial Predictors and Effect Modifiers in Generalized Linear Models

Varying-coefficient models with categorical effect modifiers are considered within the framework of generalized linear models. We distinguish between nominal and ordinal effect modifiers, and propose adequate Lasso-type regularization techniques that allow for (1) selection of relevant covariates, and (2) identification of coefficient functions that are actually varying with the level of a potentially effect modifying factor. We investigate large sample properties, and show in simulation studies that the proposed approaches perform very well for finite samples, too. In addition, the presented methods are compared with alternative procedures, and applied to real-world medical data

A General Family of Penalties for Combining Differing Types of Penalties in Generalized Structured Models

Penalized estimation has become an established tool for regularization and model selection in regression models. A variety of penalties with specific features are available and effective algorithms for specific penalties have been proposed. But not much is available to fit models that call for a combination of different penalties. When modeling rent data, which will be considered as an example, various types of predictors call for a combination of a Ridge, a grouped Lasso and a Lasso-type penalty within one model. Algorithms that can deal with such problems, are in demand. We propose to approximate penalties that are (semi-)norms of scalar linear transformations of the coefficient vector in generalized structured models. The penalty is very general such that the Lasso, the fused Lasso, the Ridge, the smoothly clipped absolute deviation penalty (SCAD), the elastic net and many more penalties are embedded. The approximation allows to combine all these penalties within one model. The computation is based on conventional penalized iteratively re-weighted least squares (PIRLS) algorithms and hence, easy to implement. Moreover, new penalties can be incorporated quickly. The approach is also extended to penalties with vector based arguments; that is, to penalties with norms of linear transformations of the coefficient vector. Some illustrative examples and the model for the Munich rent data show promising results

Science-Technology Division

The objectives of the Science-Technology Division shall be to draw together those members of the Special Libraries Association having an interest in the role of library and information science as applied to the recording, retrieval and dissemination of knowledge and information in all areas of science and technology, and to promote and improve the communication, dissemination and use of such knowledge for the benefit of libraries and their users

Science-Technology Division

Sci-Tech Division news from the Chair. An overview of 2012

Science-Technology Division

The Chair\u27s column talking about what is happening within the division and the upcoming 2012 Conference in Chicago

Seconds-scale coherence in a tweezer-array optical clock

Optical clocks based on atoms and ions achieve exceptional precision and accuracy, with applications to relativistic geodesy, tests of relativity, and searches for dark matter. Achieving such performance requires balancing competing desirable features, including a high particle number, isolation of atoms from collisions, insensitivity to motional effects, and high duty-cycle operation. Here we demonstrate a new platform based on arrays of ultracold strontium atoms confined within optical tweezers that realizes a novel combination of these features by providing a scalable platform for isolated atoms that can be interrogated multiple times. With this tweezer-array clock, we achieve greater than 3 second coherence times and record duty cycles up to 96%, as well as stability commensurate with leading platforms. By using optical tweezer arrays --- a proven platform for the controlled creation of entanglement through microscopic control --- this work further promises a new path toward combining entanglement enhanced sensitivities with the most precise optical clock transitions

Science-Technology Division

This article includes an overview of the Sci-Tech Division sessions that will take place at the 2011 SLA Conference in Philadelphia, as well as a listing of new Science-Technology Division members and a call for candidates for the Science-Technology Division Board

Science-Technology Division

Division news from the Chai