8,203 research outputs found
Confidence Corridors for Multivariate Generalized Quantile Regression
We focus on the construction of confidence corridors for multivariate
nonparametric generalized quantile regression functions. This construction is
based on asymptotic results for the maximal deviation between a suitable
nonparametric estimator and the true function of interest which follow after a
series of approximation steps including a Bahadur representation, a new strong
approximation theorem and exponential tail inequalities for Gaussian random
fields. As a byproduct we also obtain confidence corridors for the regression
function in the classical mean regression. In order to deal with the problem of
slowly decreasing error in coverage probability of the asymptotic confidence
corridors, which results in meager coverage for small sample sizes, a simple
bootstrap procedure is designed based on the leading term of the Bahadur
representation. The finite sample properties of both procedures are
investigated by means of a simulation study and it is demonstrated that the
bootstrap procedure considerably outperforms the asymptotic bands in terms of
coverage accuracy. Finally, the bootstrap confidence corridors are used to
study the efficacy of the National Supported Work Demonstration, which is a
randomized employment enhancement program launched in the 1970s. This article
has supplementary materials
A Novel Three-Point Modulation Technique for Fractional-N Frequency Synthesizer Applications
This paper presents a novel three-point modulation technique for fractional-N frequency synthesizer applications. Convention modulated fractional-N frequency synthesizers suffer from quantization noise, which degrades not only the phase noise performance but also the modulation quality. To solve this problem, this work proposes a three-point modulation technique, which not only cancels the quantization noise, but also markedly boosts the channel switching speed. Measurements reveal that the implemented 2.4 GHz fractional-N frequency synthesizer using three-point modulation can achieve a 2.5 Mbps GFSK data rate with an FSK error rate of only 1.4 %. The phase noise is approximately -98 dBc/Hz at a frequency offset of 100 kHz. The channel switching time is only 1.1 μs with a frequency step of 80 MHz. Comparing with conventional two-point modulation, the proposed three-point modulation greatly improves the FSK error rate, phase noise and channel switching time by about 10 %, 30 dB and 126 μs, respectively
Use of Devolved Controllers in Data Center Networks
In a data center network, for example, it is quite often to use controllers
to manage resources in a centralized man- ner. Centralized control, however,
imposes a scalability problem. In this paper, we investigate the use of
multiple independent controllers instead of a single omniscient controller to
manage resources. Each controller looks after a portion of the network only,
but they together cover the whole network. This therefore solves the
scalability problem. We use flow allocation as an example to see how this
approach can manage the bandwidth use in a distributed manner. The focus is on
how to assign components of a network to the controllers so that (1) each
controller only need to look after a small part of the network but (2) there is
at least one controller that can answer any request. We outline a way to
configure the controllers to fulfill these requirements as a proof that the use
of devolved controllers is possible. We also discuss several issues related to
such implementation.Comment: Appears in INFOCOM 2011 Cloud Computing Worksho
Quantile Regression in Risk Calibration
Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a stressful situation for one market participant, one likes to measure how this stress affects other factors. The CoVaR (Conditional VaR) framework has been developed for this purpose. The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor. Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semiparametric modeling framework for CoVaR is proposed. A partial linear model (PLM) is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk (MCR) defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.CoVaR, Value-at-Risk, quantile regression, locally linear quantile regression, partial linear model, semiparametric model
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