305,478 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
On Selecting and Conditioning in Multiple Testing and Selective Inference
No full textWe investigate a class of methods for selective inference that condition on a
selection event. Such methods follow a two-stage process. First, a data-driven
(sub)collection of hypotheses is chosen from some large universe of hypotheses.
Subsequently, inference takes place within this data-driven collection,
conditioned on the information that was used for the selection. Examples of
such methods include basic data splitting, as well as modern data carving
methods and post-selection inference methods for lasso coefficients based on
the polyhedral lemma. In this paper, we adopt a holistic view on such methods,
considering the selection, conditioning, and final error control steps together
as a single method. From this perspective, we demonstrate that multiple testing
methods defined directly on the full universe of hypotheses are always at least
as powerful as selective inference methods based on selection and conditioning.
This result holds true even when the universe is potentially infinite and only
implicitly defined, such as in the case of data splitting. We provide a
comprehensive theoretical framework, along with insights, and delve into
several case studies to illustrate instances where a shift to a non-selective
or unconditional perspective can yield a power gain
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Simultaneous confidence intervals for ranks using the partitioning principle
No full textWe consider the problem of constructing simultaneous confidence intervals (CIs) for the ranks of n means based on their estimates together with the (known) standard errors of those estimates. We present a generic method based on the partitioning principle in which the parameter space is partitioned into disjoint subsets and then each one of them is tested at level a. The resulting CIs have then a simultaneous coverage of 1 - alpha. We show that any procedure which produces simultaneous CIs for ranks can be written as a partitioning procedure. We present a first example where we test the partitions using the likelihood ratio (LR) test. Then, in a second example we show that a recently proposed method for simultaneous CIs for ranks using Tukey's honest significant difference test has an equivalent procedure based on the partitioning principle. By embedding these two methods inside our generic partitioning procedure, we obtain improved variants. We illustrate the performance of these methods through simulations and real data analysis on hotel ratings. While the novel method that uses the LR test and its variant produce shorter CIs when the number of means is small, the Tukey-based method and its variant produce shorter CIs when the number of means is high.Development and application of statistical models for medical scientific researc
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
Permutation-Based True Discovery Guarantee by Sum Tests
No full textSum-based global tests are highly popular in multiple hypothesis testing. In
this paper we propose a general closed testing procedure for sum tests, which
provides lower confidence bounds for the proportion of true discoveries (TDP),
simultaneously over all subsets of hypotheses. These simultaneous inferences
come for free, i.e., without any adjustment of the alpha-level, whenever a
global test is used. Our method allows for an exploratory approach, as
simultaneity ensures control of the TDP even when the subset of interest is
selected post hoc. It adapts to the unknown joint distribution of the data
through permutation testing. Any sum test may be employed, depending on the
desired power properties. We present an iterative shortcut for the closed
testing procedure, based on the branch and bound algorithm, which converges to
the full closed testing results, often after few iterations; even if it is
stopped early, it controls the TDP. We compare the properties of different
choices for the sum test through simulations, then we illustrate the
feasibility of the method for high dimensional data on brain imaging and
genomics data.Comment: Main: 27 pages, 3 figures. Appendices: 19 pages, 7 figure
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Mining e-mail content for author identification forensics
No full textWe describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different email topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation
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