29,678 research outputs found
The PyCBC search for gravitational waves from compact binary coalescence
We describe the PyCBC search for gravitational waves from compact-object
binary coalescences in advanced gravitational-wave detector data. The search
was used in the first Advanced LIGO observing run and unambiguously identified
two black hole binary mergers, GW150914 and GW151226. At its core, the PyCBC
search performs a matched-filter search for binary merger signals using a bank
of gravitational-wave template waveforms. We provide a complete description of
the search pipeline including the steps used to mitigate the effects of noise
transients in the data, identify candidate events and measure their statistical
significance. The analysis is able to measure false-alarm rates as low as one
per million years, required for confident detection of signals. Using data from
initial LIGO's sixth science run, we show that the new analysis reduces the
background noise in the search, giving a 30% increase in sensitive volume for
binary neutron star systems over previous searches.Comment: 29 pages, 7 figures, accepted by Classical and Quantum Gravit
Real-time inflation forecast densities from ensemble phillips curves
A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries
Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts
This paper reviews recently proposed likelihood ratio tests of goodness-of-fit and independence of interval forecasts. It recasts them in the framework of Pearson chi-squared statistics, and extends them to density forecasts. Two further recent developments are also incorporated, namely a more informative decomposition of the goodness-of-fit statistic, and the calculation of exact P-values. Examples considered are the US Survey of Professional Forecasters density forecasts of inflation and the Bank of England fan charts. This first evaluation of the Bank forecasts finds that the fan charts fan out too quickly, and the excessive concern with the upside risks was not justified.
Fast Two-Sample Testing with Analytic Representations of Probability Measures
We propose a class of nonparametric two-sample tests with a cost linear in
the sample size. Two tests are given, both based on an ensemble of distances
between analytic functions representing each of the distributions. The first
test uses smoothed empirical characteristic functions to represent the
distributions, the second uses distribution embeddings in a reproducing kernel
Hilbert space. Analyticity implies that differences in the distributions may be
detected almost surely at a finite number of randomly chosen
locations/frequencies. The new tests are consistent against a larger class of
alternatives than the previous linear-time tests based on the (non-smoothed)
empirical characteristic functions, while being much faster than the current
state-of-the-art quadratic-time kernel-based or energy distance-based tests.
Experiments on artificial benchmarks and on challenging real-world testing
problems demonstrate that our tests give a better power/time tradeoff than
competing approaches, and in some cases, better outright power than even the
most expensive quadratic-time tests. This performance advantage is retained
even in high dimensions, and in cases where the difference in distributions is
not observable with low order statistics
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Threshold quantile autoregressive models
We study in this article threshold quantile autoregressive processes. In particular we propose estimation and inference of the parameters in nonlinear quantile processes when the threshold parameter defining nonlinearities is known for each quantile, and also when the parameter vector is estimated consistently. We derive the asymptotic properties of the nonlinear threshold quantile autoregressive estimator. In addition, we develop hypothesis tests for detecting threshold nonlinearities in the quantile process when the threshold parameter vector is not identified under the null hypothesis. In this case we propose to approximate the asymptotic distribution of the composite test using a p-value transformation. This test contributes to the literature on nonlinearity tests by extending Hansen’s (Econometrica 64, 1996, pp.413-430) methodology for the conditional mean process to the entire quantile process. We apply the proposed methodology to model the dynamics of US unemployment growth after the Second World War. The results show evidence of important heterogeneity associated with unemployment, and strong asymmetric persistence on unemployment growth
Children’s improvement of a motor response during backward falls through the implementation of a safe fall program
The World Health Organization has warned that, in children, the second cause of death from unintentional injuries are falls. The objective of this study was to analyze the motor response of primary schoolchildren when a backwards fall occurs. These analyses occurred before and after interventions of the Safe Fall program, which aims to teach safe and protected ways of backward falling. A quasi-experimental research design was used, with a sample of 122 Spanish (Sevillian) schoolchildren in the 10–12 age bracket. The INFOSECA ad-hoc observation scale was used for data
collection: this scale registers 5 essential physical reactions throughout the process of a safe and protected backwards fall. After that, a number of descriptive, correlational and contrast statistics were applied. The value used in the McNemar test to establish statistical significance was p < 0.05. Results
showed that over 85% of students had developed the competence to correctly perform all five physical motions that allow for a safer backward fall. The teaching of safe and protected techniques for falling backwards in child population in Primary Education is possible through the implementation of the
Safe Fall program in Physical Education classes, which can help making falls safer, diminishing the risk and severity of the injuries they cause
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