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A Residual Based Multiple Testing Procedure for Variance Changepoints
Multiple testing Admissibility Multiple change points Variance change
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2016/1/19
In this paper, we approach the variance changepoints detection problem from a multiple testing setting. After proving that the standard Step-Up and Step-Down procedures are inadmissible for the standa...
Robust Minimum Variance Beamforming
Ellipsoidal calculus Hadamard product robust beamforming second-order cone programming
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2015/7/10
This paper introduces an extension of minimum variance beamforming, also known as Capon's method, that explicitly takes into account variation or uncertainty in the assumed array response. Sources of ...
Derandomizing and Rerandomizing Variance Estimators
Derandomizing Rerandomizing Variance Estimators
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2015/7/8
This technical report is meant to accompany the paper [7] and should be read in conjuction with that work. It describes several concepts which were alluded to in [7] but not elaborated on.
We give al...
Derandomizing Variance Estimators
Derandomizing Variance Estimators
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2015/7/8
One may consider a discrete-event simulation as a Markov chain evolving on a suitably rich state space. One way that regenerative cycles may be constructed for general state-space Markov chains is to ...
Approximating Martingales for Variance Reduction in Markov Process Simulation
Variance reduction Markov process simulation martingale
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2015/7/8
“Knowledge of either analytical or numerical approximations should enable more efficient simulation estimators to be constructed.” This principle seems intuitively plausible and certainly attractive, ...
A Comparison of Cross-Entropy and Variance Minimization Strategies
variance minimization cross-entropy importance sampling rareevent simulation likelihood ratio degeneracy
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2015/7/6
The variance minimization (VM) and cross-entropy (CE) methods are two versatile adaptive importance sampling procedures that have been successfully applied to a wide variety of difficult rare-event es...
Zero-Variance Importance Sampling Estimators for Markov Process Expectations
Importance sampling Markov process simulation
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2015/7/6
We consider the use of importance sampling to compute expectations of functionals of Markov processes. For a class of expectations that can be characterized as positive solutions to a linear system, w...
Parametric Stein operators and variance bounds
Chernoff inequality Cramer-Rao inequality parameter of interest Stein charac-terization Stein’s method
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2013/6/17
Stein operators are differential operators which arise within the so-called Stein's method for stochastic approximation. We propose a new mechanism for constructing such operators for arbitrary (conti...
Warped Functional Analysis of Variance
Karhunen–Lo`eve decomposition longitudinal data phasevariability quan-titative genetics random-effect models
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2013/4/28
This article presents a functional Analysis of Variance model that explicitly incorporates phase variability through a time-warping component, allowing for a unified and parsimonious approach to estim...
Variance estimation for Brier Score decomposition
Variance estimation Brier Score decomposition
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2013/4/28
The Brier Score is a widely-used criterion to assess the quality of probabilistic predictions of binary events. The expectation value of the Brier Score can be decomposed into the sum of three compone...
Multifidelity variance reduction for pick-freeze Sobol index estimation
Multifidelity variance reduction pick-freeze Sobol index estimation
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2013/4/28
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
A multiple filter test for change point detection in renewal processes with varying variance
A multiple filter test change point detection renewal processes varying variance
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2013/4/27
Non-stationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train record...
Variance estimation and asymptotic confidence bands for the mean estimator of sampled functional data with high entropy unequal probability sampling designs
covariance function finite population Hajek approximation Horvitz-Thompso estimator Kullback-Leibler divergence rejective sampling unequal probability sampling without replacement
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2012/11/23
For fixed size sampling designs with high entropy it is well known that the variance of the Horvitz-Thompson estimator can be approximated by the H\'ajek formula. The interest of this asymptotic varia...
Diagnostic Tests for Non-causal Time Series with Infinite Variance
Non-causal AR Process Infinite Variance Goodness-of-fit Portmanteau Test alpha-stabledistribution
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2012/11/22
We study goodness-of-fit testing for non-causal autoregressive time series with non-Gaussian stable noise. To model time series exhibiting sharp spikes or occasional bursts of outlying observations, t...
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
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2012/11/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...