Frequentist statistics as a theory of inductive inference

After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of $p$-values rather than as formal procedures for ``acceptance'' and ``rejection.'' A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of $p$-values in the specific circumstances of each application, as contrasted with a long-run interpretation. A variety of more complicated situations are discussed in which modification of the simple $p$-value may be essential.Comment: Published at http://dx.doi.org/10.1214/074921706000000400 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Can co-location be used as a proxy for face-to-face contacts?

Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies

Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-scale dataset and design an automatic evaluation methodology. Our knowledge base completion method uses information within the existing KB and external information from Wikipedia. We show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods. We also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method.Comment: North American Chapter of the Association for Computational Linguistics- Human Language Technologies, 201

REPdenovo: Inferring De Novo Repeat Motifs from Short Sequence Reads.

Repeat elements are important components of eukaryotic genomes. One limitation in our understanding of repeat elements is that most analyses rely on reference genomes that are incomplete and often contain missing data in highly repetitive regions that are difficult to assemble. To overcome this problem we develop a new method, REPdenovo, which assembles repeat sequences directly from raw shotgun sequencing data. REPdenovo can construct various types of repeats that are highly repetitive and have low sequence divergence within copies. We show that REPdenovo is substantially better than existing methods both in terms of the number and the completeness of the repeat sequences that it recovers. The key advantage of REPdenovo is that it can reconstruct long repeats from sequence reads. We apply the method to human data and discover a number of potentially new repeats sequences that have been missed by previous repeat annotations. Many of these sequences are incorporated into various parasite genomes, possibly because the filtering process for host DNA involved in the sequencing of the parasite genomes failed to exclude the host derived repeat sequences. REPdenovo is a new powerful computational tool for annotating genomes and for addressing questions regarding the evolution of repeat families. The software tool, REPdenovo, is available for download at https://github.com/Reedwarbler/REPdenovo