209,444 research outputs found

    Preconference Training Workshop: Multistate analysis of life histories with R

    Get PDF
    Multistate Analysis of Life Histories with R Frans Willekens, Netherlands Interdisciplinary Demographic Institute and Max Planck Institute for Demographic Research Workshop Venue: Rideau Salon Workshop Outline A. Introduction Multistate models describe the life course in terms of transitions individuals experience as they go through stages of life and move between states. These states may represent health states, family status, occupation, place of residence, education or other domains of life. Multistate models have been successfully used in a wide variety of applied sciences. The most fruitful areas of application are health sciences, demography and economics. Important examples of applications of multistate models are stem cell transplantation (with disease relapse and death as endpoints and graft-versus-host disease as intermediate states), estimation of healthy life expectancy, marital careers, migration histories, and participation in the labor market. These applications have in common a fundamental interest in competing risks, event histories and state sequences. The subject of the workshop is the modeling of life histories. Multistate analysis of life histories with R is an introduction to multistate event history analysis. It is an extension of survival analysis, in which a single terminal event (endpoint) is considered and the time-to-event is studied. Life histories are modeled as realizations of continuous-time Markov processes (and extensions). The statistical theory of counting processes emerged as the dominant theory for estimating transition rates from data on event counts and populations at risk. Non-parametric and parametric methods have been developed. In recent years, software packages for multistate modeling have become available. R is the language of choice and the Comprehensive R Archive Network (CRAN) is the main repository. The packages are free and the source code is available in CRAN. The packages include survival, eha, Epi, mvna, etm, mstate, msm, Biograph, MicSim andTraMineR. For a recent review, see Willekens and Putter (2014) Software for multistate analysis, Demographic Research 31(14):381-420, and the CRAN Task View on Survival Analysis In 2011, the Journal of Statistical Software published a special issue on multistate modeling (H. Putter, editor). Multistate modeling is an active area of research across disciplines. The research benefits from the current interest in prognostic modeling (of outcomes of health conditions and behavior/lifestyle) and predictive analytics

    A probabilistic justification for using tf.idf term weighting in information retrieval

    Get PDF
    This paper presents a new probabilistic model of information retrieval. The most important modeling assumption made is that documents and queries are defined by an ordered sequence of single terms. This assumption is not made in well known existing models of information retrieval, but is essential in the field of statistical natural language processing. Advances already made in statistical natural language processing will be used in this paper to formulate a probabilistic justification for using tf.idf term weighting. The paper shows that the new probabilistic interpretation of tf.idf term weighting might lead to better understanding of statistical ranking mechanisms, for example by explaining how they relate to coordination level ranking. A pilot experiment on the TREC collection shows that the linguistically motivated weighting algorithm outperforms the popular BM25 weighting algorithm

    Introduction to the special issue on cross-language algorithms and applications

    Get PDF
    With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

    An Introduction to the 'Special Volume Spectroscopy and Chemometrics in R'

    Get PDF
    This special volume collates ten issues under the rubric "Spectroscopy and Chemometrics in R". In so doing, it provides an overview of the breadth, depth and state of the art of R-based software projects for spectroscopy and chemometrics applications. Just as the authors have contributed to R their documentation and source code, so has R contributed to the quality, standardization and dissemination of their software, as this volume attests. We hope that the volume is inspiring to both computational statisticians interested in applications of their methodologies and to spectroscopists or chemometricians in need of solutions to their data analysis problems.

    Introduction to papers on the modeling and analysis of network data

    Full text link
    Introduction to papers on the modeling and analysis of network dataComment: Published in at http://dx.doi.org/10.1214/10-AOAS346 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
    corecore