3,337 research outputs found
Adaptive goodness-of-fit tests based on signed ranks
Within the nonparametric regression model with unknown regression function
and independent, symmetric errors, a new multiscale signed rank statistic
is introduced and a conditional multiple test of the simple hypothesis
against a nonparametric alternative is proposed. This test is distribution-free
and exact for finite samples even in the heteroscedastic case. It adapts in a
certain sense to the unknown smoothness of the regression function under the
alternative, and it is uniformly consistent against alternatives whose sup-norm
tends to zero at the fastest possible rate. The test is shown to be
asymptotically optimal in two senses: It is rate-optimal adaptive against
H\"{o}lder classes. Furthermore, its relative asymptotic efficiency with
respect to an asymptotically minimax optimal test under sup-norm loss is close
to 1 in case of homoscedastic Gaussian errors within a broad range of
H\"{o}lder classes simultaneously.Comment: Published in at http://dx.doi.org/10.1214/009053607000000992 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Optimal Calibration for Multiple Testing against Local Inhomogeneity in Higher Dimension
Based on two independent samples X_1,...,X_m and X_{m+1},...,X_n drawn from
multivariate distributions with unknown Lebesgue densities p and q
respectively, we propose an exact multiple test in order to identify
simultaneously regions of significant deviations between p and q. The
construction is built from randomized nearest-neighbor statistics. It does not
require any preliminary information about the multivariate densities such as
compact support, strict positivity or smoothness and shape properties. The
properly adjusted multiple testing procedure is shown to be sharp-optimal for
typical arrangements of the observation values which appear with probability
close to one. The proof relies on a new coupling Bernstein type exponential
inequality, reflecting the non-subgaussian tail behavior of a combinatorial
process. For power investigation of the proposed method a reparametrized
minimax set-up is introduced, reducing the composite hypothesis "p=q" to a
simple one with the multivariate mixed density (m/n)p+(1-m/n)q as infinite
dimensional nuisance parameter. Within this framework, the test is shown to be
spatially and sharply asymptotically adaptive with respect to uniform loss on
isotropic H\"older classes. The exact minimax risk asymptotics are obtained in
terms of solutions of the optimal recovery
mAPN: Modeling, Analysis, and Exploration of Algorithmic and Parallelism Adaptivity
Using parallel embedded systems these days is increasing. They are getting
more complex due to integrating multiple functionalities in one application or
running numerous ones concurrently. This concerns a wide range of applications,
including streaming applications, commonly used in embedded systems. These
applications must implement adaptable and reliable algorithms to deliver the
required performance under varying circumstances (e.g., running applications on
the platform, input data, platform variety, etc.). Given the complexity of
streaming applications, target systems, and adaptivity requirements, designing
such systems with traditional programming models is daunting. This is why
model-based strategies with an appropriate Model of Computation (MoC) have long
been studied for embedded system design. This work provides algorithmic
adaptivity on top of parallelism for dynamic dataflow to express larger sets of
variants. We present a multi-Alternative Process Network (mAPN), a high-level
abstract representation in which several variants of the same application
coexist in the same graph expressing different implementations. We introduce
mAPN properties and its formalism to describe various local implementation
alternatives. Furthermore, mAPNs are enriched with metadata to Provide the
alternatives with quantitative annotations in terms of a specific metric. To
help the user analyze the rich space of variants, we propose a methodology to
extract feasible variants under user and hardware constraints. At the core of
the methodology is an algorithm for computing global metrics of an execution of
different alternatives from a compact mAPN specification. We validate our
approach by exploring several possible variants created for the Automatic
Subtitling Application (ASA) on two hardware platforms.Comment: 26 PAGES JOURNAL PAPE
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
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