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Decision noise may mask criterion shifts:Reply to Balakrishnan and MacDonald (2008)
Decision criterion shifts Balakrishnan MacDonald
2015/7/28
J. D. Balakrishnan and J. A. MacDonald (2008) argue that RTbased measures of signal detection processes provide evidence against signal detection theory’s notion of a flexible decision criterion.
EigenPrism:Inference for High-Dimensional Signal-to-Noise Ratios
EigenPrism High-Dimensional Signal Noise Ratios
2015/6/17
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional (p > n) regression estimator, (2) the linear...
Identification of Signal, Noise, and Indistinguishable Subsets in High-Dimensional Data Analysis
Two-Level Thresholding Signal detection False positive control False negative control Multiple testing Variable screening
2013/6/13
Motivated by applications in high-dimensional data analysis where strong signals often stand out easily and weak ones may be indistinguishable from the noise, we develop a statistical framework to pro...
Volatility Inference in the Presence of Both Endogenous Time and Microstructure Noise
It^o Process Realized Volatility Integrated Volatility Time Endogeneity Market Microstructure Noise
2013/5/2
In this article we consider the volatility inference in the presence of both market microstructure noise and endogenous time. Estimators of the integrated volatility in such a setting are proposed, an...
State estimation under non-Gaussian Levy noise: A modified Kalman filtering method
Kalman filter modified Kalman filter Non-Gaussiannoise L′evy noise state estimation data assimilation
2013/4/28
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\'evy noise is present, the conventional Kalman filter may fail to be effective d...
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
Classification Asymmetric Label Noise Consistency Maximal Denoising
2013/4/27
In many real-world classification problems, the labels of training examples are randomly corrupted. Previous theoretical work on classification with label noise assumes that the two classes are separa...
Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning
Asynchronous impulsive noise cyclostationary noise PLC OFDM sparse Bayesian learning
2013/4/27
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noi...
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
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 ...
Kramers-type effective Reactive Flow in Structured-noise Environments
Kramers-type Reactive Flow Structured-noise Environments
2012/9/17
The non-Markovian features of three typical anomalous diffusing systems are studied by analyti-cally solving the generalized Langevin equation directly driven by three kind of internal structured-nois...
Singular Vector Perturbation under Gaussian Noise
Singular Vector Perturbation Gaussian Noise
2012/9/18
We study the following problem: when a low rank matrix is perturbed by Gaus-sian noise, what is the distribution of the induced (by singular value decompostion) perturbation on its singular vectors? I...
Large information plus noise random matrix models and consistent subspace estimation in large sensor networks
Large information plus noise random matrix models consistent subspace estimation large sensor networks
2011/7/7
In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by ...
An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory
Density estimation Kullback–Leibler divergence
2011/7/6
The article is devoted to the nonparametric estimation of the quadratic covariation of non-synchronously observed It\^o processes in an additive microstructure noise model.
Robust Adaptive Rate-Optimal Testing for the White Noise Hypothesis
Noise Hypothesis HAC Inference Automatic nonparametric tests Adaptive rate-optimality
2011/7/5
A new test is proposed for the weak white noise null hypothesis. The test is based on an automatic choice of the order for a Box-Pierce or Hong test statistic.
Strictly stationary solutions of multivariate ARMA equations with i.i.d. noise
Strictly stationary solutions multivariate ARMA equations i.i.d. noise
2011/6/17
We obtain necessary and sufficient conditions for the existence of strictly stationary
solutions of multivariate ARMA equations with independent and identically
distributed noise. For general ARMA(p...
Making Tensor Factorizations Robust to Non-Gaussian Noise
Making Tensor Factorizations Robust Non-Gaussian Noise
2010/10/19
Tensors are multi-way arrays, and the Candecomp/Parafac (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective functio...