2,524 research outputs found
Nonparametric estimation of a convex bathtub-shaped hazard function
In this paper, we study the nonparametric maximum likelihood estimator (MLE)
of a convex hazard function. We show that the MLE is consistent and converges
at a local rate of at points where the true hazard function is
positive and strictly convex. Moreover, we establish the pointwise asymptotic
distribution theory of our estimator under these same assumptions. One notable
feature of the nonparametric MLE studied here is that no arbitrary choice of
tuning parameter (or complicated data-adaptive selection of the tuning
parameter) is required.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ202 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Estimation of a discrete monotone distribution
We study and compare three estimators of a discrete monotone distribution:
(a) the (raw) empirical estimator; (b) the "method of rearrangements"
estimator; and (c) the maximum likelihood estimator. We show that the maximum
likelihood estimator strictly dominates both the rearrangement and empirical
estimators in cases when the distribution has intervals of constancy. For
example, when the distribution is uniform on , the asymptotic
risk of the method of rearrangements estimator (in squared norm) is
, while the asymptotic risk of the MLE is of order .
For strictly decreasing distributions, the estimators are asymptotically
equivalent.Comment: 39 pages. See also
http://www.stat.washington.edu/www/research/reports/2009/
http://www.stat.washington.edu/jaw/RESEARCH/PAPERS/available.htm
Probing Limits of Information Spread with Sequential Seeding
We consider here information spread which propagates with certain probability
from nodes just activated to their not yet activated neighbors. Diffusion
cascades can be triggered by activation of even a small set of nodes. Such
activation is commonly performed in a single stage. A novel approach based on
sequential seeding is analyzed here resulting in three fundamental
contributions. First, we propose a coordinated execution of randomized choices
to enable precise comparison of different algorithms in general. We apply it
here when the newly activated nodes at each stage of spreading attempt to
activate their neighbors. Then, we present a formal proof that sequential
seeding delivers at least as large coverage as the single stage seeding does.
Moreover, we also show that, under modest assumptions, sequential seeding
achieves coverage provably better than the single stage based approach using
the same number of seeds and node ranking. Finally, we present experimental
results showing how single stage and sequential approaches on directed and
undirected graphs compare to the well-known greedy approach to provide the
objective measure of the sequential seeding benefits. Surprisingly, applying
sequential seeding to a simple degree-based selection leads to higher coverage
than achieved by the computationally expensive greedy approach currently
considered to be the best heuristic
Effects of the interaction between slurry, soil conditioners, and mineral NPK fertilizers on selected nutritional parameters of Festulolium braunii (K. Richt.) A. Camus
The research was aimed at assessing the biomass yield of Festulolium braunii and its
content of raw protein and crude ash after application of slurry, both on its own and together with
soil conditioners (UGmax and Humus Active), and mineral fertilizers. The studies were
conducted on the basis of a two-year field experiment. The interaction between slurry and soil
conditioners and between slurry and mineral fertilizers was studied on the Sulino variety of
Festulolium braunii, a hybrid between Lolium multiflorum and Festuca pratensis.
Compared with plants treated with liquid manure on its own, slurry applied with soil conditioners
and mineral fertilizer did not significantly increase the biomass yield of the grass. However, there
was higher protein content in Festulolium braunii, even if statistically insignificant, as a response
to slurry supplemented with mineral fertilizer than in plants treated with slurry only. Various
forms of treatment did not differentiate crude ash content in plant dry matter in a statistically
significant way
AirNet: Neural Network Transmission over the Air
State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, the employed DNNs are location- and time-dependent, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. This can be considered as a joint source-channel coding (JSCC) problem, in which the goal is not to recover the DNN coefficients with the minimal distortion, but in a manner that provides the highest accuracy in the downstream task. For this purpose we introduce AirNet, a novel training and analog transmission method to deliver DNNs over the air. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to identify the most significant DNN parameters that can be delivered within the available channel bandwidth, knowledge distillation, and nonlinear bandwidth expansion to provide better error protection for the most important network parameters. We show that AirNet achieves significantly higher test accuracy compared to the separation-based alternative, and exhibits graceful degradation with channel quality
Interacting Spreading Processes in Multilayer Networks: A Systematic Review
© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward
Convergence of linear functionals of the Grenander estimator under misspecification
Under the assumption that the true density is decreasing, it is well known
that the Grenander estimator converges at rate if the true density is
curved [Sankhy\={a} Ser. A 31 (1969) 23-36] and at rate if the
density is flat [Ann. Probab. 11 (1983) 328-345; Canad. J. Statist. 27 (1999)
557-566]. In the case that the true density is misspecified, the results of
Patilea [Ann. Statist. 29 (2001) 94-123] tell us that the global convergence
rate is of order in Hellinger distance. Here, we show that the local
convergence rate is at a point where the density is misspecified.
This is not in contradiction with the results of Patilea [Ann. Statist. 29
(2001) 94-123]: the global convergence rate simply comes from locally curved
well-specified regions. Furthermore, we study global convergence under
misspecification by considering linear functionals. The rate of convergence is
and we show that the limit is made up of two independent terms: a
mean-zero Gaussian term and a second term (with nonzero mean) which is present
only if the density has well-specified locally flat regions.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1196 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
IMPACT: The Journal of the Center for Interdisciplinary Teaching and Learning. Volume 8, Issue 1, Winter 2019
IMPACT: The Journal of the Center for Interdisciplinary Teaching & Learning is a peer-reviewed, biannual online journal that publishes scholarly and creative non-fiction essays about the theory, practice and assessment of interdisciplinary education. Impact is produced by the Center for Interdisciplinary Teaching & Learning at the College of General Studies, Boston University (www.bu.edu/cgs/citl).In this issue of Impact you will find a humanities scholar deeply engaged with the arcing out of a new territory: the interdisciplinary study of the Grateful Dead. Impactâs own Christopher Coffmanâs review essay should be required reading for scholars of popular music, performance studies and history. His review also serves as an important reference for those who aspire to teach a course on the Grateful Dead, as well as for those who wish to write review essays. In this issue we also hear from those who are engaged in teaching people who are incarcerated. Importantly, Stephanie Cageâs essay looks to incarcerated people themselves to find out what they think about prison education. Peter Wakefield encourages us to see The Great Gatsby anew, in particular in the context of American racism and White supremacy. Wakefieldâs essay is important too because it had its genesis in Writing, the State, and the Rise of Neo-Nationalism: Historical Contexts and Contemporary Concerns, a conference sponsored by the Center for Interdisciplinary Teaching & Learning
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