179,831 research outputs found
Department of Education, Iowa Vocational Rehabilitation Services Performance Plan, FY 2004
Agency Performance Plan, Department of Education, Iowa Vocational Rehabilitation Service
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Director Reputation, Ceo-Board Power, And The Dynamics Of Board Interlocks
This study advances research on CEO-board relationships, interlocking directorates, and director reputation by examining how contests for intraorganizational power can affect interorganizational ties. We propose that powerful top managers seek to maintain their control by selecting and retaining board members with experience on other, passive boards and excluding individuals with experience on more active boards. We also propose that powerful boards similarly seek to maintain their control by favoring directors with a reputation for more actively monitoring management and avoiding directors with experience on passive boards. Hypotheses are tested longitudinally using CEO-board data taken from 491 of the largest U.S. corporations over a recent seven-year period. The findings suggest that variation in CEO-board power relationships across organizations has contributed to a segmentation of the corporate director network. We discuss how our perspective can reconcile contrary views and debates on whether increased board control has diffused across large U.S. corporations.(.)Managemen
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
One of the most challenging tasks when adopting Bayesian Networks (BNs) is
the one of learning their structure from data. This task is complicated by the
huge search space of possible solutions, and by the fact that the problem is
NP-hard. Hence, full enumeration of all the possible solutions is not always
feasible and approximations are often required. However, to the best of our
knowledge, a quantitative analysis of the performance and characteristics of
the different heuristics to solve this problem has never been done before.
For this reason, in this work, we provide a detailed comparison of many
different state-of-the-arts methods for structural learning on simulated data
considering both BNs with discrete and continuous variables, and with different
rates of noise in the data. In particular, we investigate the performance of
different widespread scores and algorithmic approaches proposed for the
inference and the statistical pitfalls within them
Analysis of dependence among size, rate and duration in internet flows
In this paper we examine rigorously the evidence for dependence among data
size, transfer rate and duration in Internet flows. We emphasize two
statistical approaches for studying dependence, including Pearson's correlation
coefficient and the extremal dependence analysis method. We apply these methods
to large data sets of packet traces from three networks. Our major results show
that Pearson's correlation coefficients between size and duration are much
smaller than one might expect. We also find that correlation coefficients
between size and rate are generally small and can be strongly affected by
applying thresholds to size or duration. Based on Transmission Control Protocol
connection startup mechanisms, we argue that thresholds on size should be more
useful than thresholds on duration in the analysis of correlations. Using
extremal dependence analysis, we draw a similar conclusion, finding remarkable
independence for extremal values of size and rate.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS268 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models
Exponential-family random graph models (ERGMs) provide a principled way to
model and simulate features common in human social networks, such as
propensities for homophily and friend-of-a-friend triad closure. We show that,
without adjustment, ERGMs preserve density as network size increases. Density
invariance is often not appropriate for social networks. We suggest a simple
modification based on an offset which instead preserves the mean degree and
accommodates changes in network composition asymptotically. We demonstrate that
this approach allows ERGMs to be applied to the important situation of
egocentrically sampled data. We analyze data from the National Health and
Social Life Survey (NHSLS).Comment: 37 pages, 2 figures, 5 tables; notation revised and clarified, some
sections (particularly 4.3 and 5) made more rigorous, some derivations moved
into the appendix, typos fixed, some wording change
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
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