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3,361
Estimating functions for noisy observations of ergodic diffusions
, 2010
"... In this article, general estimating functions for ergodic diffusions sampled at high frequency with noisy observations are presented. The theory is formulated in term of approximate martingale estimating functions based on local means of the observations, and simple conditions are given for rate ..."
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In this article, general estimating functions for ergodic diffusions sampled at high frequency with noisy observations are presented. The theory is formulated in term of approximate martingale estimating functions based on local means of the observations, and simple conditions are given for rate
Efficient estimation of stochastic volatility using noisy observations: A multiscale approach
, 2004
"... With the availability of high frequency financial data, nonparametric estimation of volatility of an asset return process becomes feasible. A major problem is how to estimate the volatility consistently and efficiently, when the observed asset returns contain error or noise, for example, in the form ..."
Abstract

Cited by 154 (14 self)
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With the availability of high frequency financial data, nonparametric estimation of volatility of an asset return process becomes feasible. A major problem is how to estimate the volatility consistently and efficiently, when the observed asset returns contain error or noise, for example
Perfect Simulation of Point Processes Given Noisy Observations
, 2001
"... The paper is concerned with the exact simulation of an unobserved true point process conditional on a noisy observation. We use dominated coupling from the past (CFTP) on an augmented state space to produce perfect samples of the target marked point process. An optimized coupling of the target chain ..."
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Cited by 3 (0 self)
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The paper is concerned with the exact simulation of an unobserved true point process conditional on a noisy observation. We use dominated coupling from the past (CFTP) on an augmented state space to produce perfect samples of the target marked point process. An optimized coupling of the target
Perfect Simulation of Point Patterns From Noisy Observations
, 2000
"... The paper is concerned with the Bayesian analysis of point processes which are observed with noise. It is shown how to produce exact samples from the posterior distribution of the unobserved true point pattern given a noisy observation. The algorithm is a perfect simulation method which applies d ..."
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Cited by 1 (0 self)
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The paper is concerned with the Bayesian analysis of point processes which are observed with noise. It is shown how to produce exact samples from the posterior distribution of the unobserved true point pattern given a noisy observation. The algorithm is a perfect simulation method which applies
Parameter estimation from noisy observation of imputs and outputs
"... Citation for published version (APA): Vregelaar, ten, J. M. (1988). Parameter estimation from noisy observation of imputs and outputs. (Memorandum COSOR; Vol. 8813). Eindhoven: Technische Universiteit Eindhoven. Document status and date: Published: 01/01/1988 Document Version: Publisher's PDF ..."
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Citation for published version (APA): Vregelaar, ten, J. M. (1988). Parameter estimation from noisy observation of imputs and outputs. (Memorandum COSOR; Vol. 8813). Eindhoven: Technische Universiteit Eindhoven. Document status and date: Published: 01/01/1988 Document Version: Publisher
Asymptotic equivalence for inference on the volatility from noisy observations
 Ann. Statist
"... ar ..."
Tracking Stopping Times Through Noisy Observations
, 2008
"... A novel quickest detection setting is proposed which is a generalization of the wellknown Bayesian changepoint detection model. Suppose {(Xi, Yi)}i≥1 is a sequence of pairs of random variables, and that S is a stopping time with respect to {Xi}i≥1. The problem is to find a stopping time T with resp ..."
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Cited by 4 (2 self)
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A novel quickest detection setting is proposed which is a generalization of the wellknown Bayesian changepoint detection model. Suppose {(Xi, Yi)}i≥1 is a sequence of pairs of random variables, and that S is a stopping time with respect to {Xi}i≥1. The problem is to find a stopping time T with respect to {Yi}i≥1 that optimally tracks S, in the sense that T minimizes the expected reaction delay E(T − S) +, while keeping the falsealarm probability P(T < S) below a given threshold α ∈ [0, 1]. This problem formulation applies in several areas, such as in communication, detection, forecasting, and quality control. Our results relate to the situation where the Xi’s and Yi’s take values in finite alphabets and where S is bounded by some positive integer κ. By using elementary methods based on the analysis of the tree structure of stopping times, we exhibit an algorithm that computes the optimal average reaction delays for all α ∈ [0, 1], and constructs the associated optimal stopping times T. Under certain conditions on {(Xi, Yi)}i≥1 and S, the algorithm running time is polynomial in κ.
FROM NOISY OBSERVATIONS: LOCAL METHOD OF MOMENTS AND EFFICIENCY
"... An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and nonsynchronous observations under highfrequency asymptotics. Our approach relies on an asymptotically equivalent continuoustime observat ..."
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An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and nonsynchronous observations under highfrequency asymptotics. Our approach relies on an asymptotically equivalent continuous
IDENTIFYING GRAPHS FROM NOISY OBSERVATIONAL DATA
, 2012
"... There is a growing amount of data describing networks – examples include social networks, communication networks, and biological networks. As the amount of available data increases, so does our interest in analyzing the properties and characteristics of these networks. However, in most cases the dat ..."
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the data is noisy, incomplete, and the result of passively acquired observational data; naively analyzing these networks without taking these errors into account can result in inaccurate and misleading conclusions. In my dissertation, I study the tasks of entity resolution, link prediction, and collective
Results 1  10
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