How powerful are data driven score tests for uniformity

Large data sets are powerful.

The problems with large data sets highlighted by Drake and McHugo—poor-quality data, statistical significance without meaningfulness, multiple tests, and post hoc interpretations—are neither new nor unique to large data sets. In addition, not all administrative data sets are necessarily large. It is difficult to find good data, administrative or otherwise, and to use it appropriately. Researche...

How powerful are the DDH hard groups?

The question whether Identity-Based Encryption (IBE) can be based on the Decisional DiffieHellman (DDH) assumption is one of the most prominent questions in Cryptography related to DDH. We study limitations on the use of the DDH assumption in cryptographic constructions, and show that it is impossible to construct a secure Identity-Based Encryption system using, in a black box way, only the DDH...

Laboratory tests for SARS: powerful or peripheral?

How To Obtain Powerful Parsers That Are Elegant and Practical

Most of the functional parsers published today focus on elegance. But they pay for this elegance by neglecting practical requirements such as efficiency and decent error handling. By contrast, a traditional parser (generator) based on LLor LR-principles focuses on efficiency and neglects issues such as elegance and understandability almost entirely. In this paper we combine these pragmatic issu...

How good are quantitative complementation tests?

Several recent studies have used quantitative complementation tests to identify relatively short chromosome regions that contain genes that influence life span and to screen for candidate life-span genes in flies. The methodology and logic of quantitative complementation tests are described. Arguments are presented that suggest that these tests may be misleading because there is a substantial, ...