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30
Online Fault Detection of Sensor Measurements
 IEEE Sensors
, 2003
"... Online fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classication of faults in sensor networks and the rst online modelbased testing t ..."
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Cited by 30 (0 self)
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Online fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classication of faults in sensor networks and the rst online modelbased testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a exible tradeoff between accuracy and latency. The key idea is to formulate online testing as a set of instances of a nonlinear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors.
Principal Component Analysis
 (IN PRESS, 2010). WILEY INTERDISCIPLINARY REVIEWS: COMPUTATIONAL STATISTICS, 2
, 2010
"... Principal component analysis (pca) is a multivariate technique that analyzes a data table in which observations are described by several intercorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal var ..."
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Cited by 25 (5 self)
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Principal component analysis (pca) is a multivariate technique that analyzes a data table in which observations are described by several intercorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the pca model can be evaluated using crossvalidation techniques such as the bootstrap and the jackknife. Pca can be generalized as correspondence analysis (ca) in order to handle qualitative variables and as multiple factor analysis (mfa) in order to handle heterogenous sets of variables. Mathematically, pca depends upon the eigendecomposition of positive semidefinite matrices and upon the singular value decomposition (svd) of rectangular matrices.
The differential privacy frontier (extended abstract
 In TCC
, 2009
"... Abstract. We review the definition of differential privacy and briefly survey a handful of very recent contributions to the differential privacy frontier. 1 Background Differential privacy is a strong privacy guarantee for an individual’s input to a (randomized) function or sequence of functions, wh ..."
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Cited by 24 (0 self)
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Abstract. We review the definition of differential privacy and briefly survey a handful of very recent contributions to the differential privacy frontier. 1 Background Differential privacy is a strong privacy guarantee for an individual’s input to a (randomized) function or sequence of functions, which we call a privacy mechanism. Informally, the guarantee says that the behavior of the mechanism is essentially unchanged independent of whether any individual opts into or opts out of the data set. Designed for statistical analysis, for example, of health or census data, the definition protects the privacy of individuals, and small groups of individuals, while permitting very different outcomes in the case of very different data sets. We begin by recalling some differential privacy basics. While the frontier of a vibrant area is always in flux, we will endeavor to give an impression of the state of the art by surveying a handful of extremely recent advances
The Impact of Bootstrap Methods on Time Series Analysis
 Statistical Science
, 2003
"... Sparked by Efron’s seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data— mainly i.i.d. or regression setups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random field ..."
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Cited by 21 (5 self)
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Sparked by Efron’s seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data— mainly i.i.d. or regression setups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random fields. Consequently, the availability of valid nonparametric inference procedures based on resampling and/or subsampling has freed practitioners from the necessity of resorting to simplifying assumptions such as normality or linearity that may be misleading.
Robust Estimation of Correlation with Applications to Computer Vision
 Pattern Recognition
, 1995
"... In this paper we compare to the standard correlation coefficient three estimators of similarity for visual patterns which are based on the L 2 and L 1 norms. The emphasis of the comparison is on the stability of the resulting estimates. Bias, efficiency, normality and robustness are investigated thr ..."
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Cited by 10 (3 self)
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In this paper we compare to the standard correlation coefficient three estimators of similarity for visual patterns which are based on the L 2 and L 1 norms. The emphasis of the comparison is on the stability of the resulting estimates. Bias, efficiency, normality and robustness are investigated through Monte Carlo simulations in a statistical task, the estimation of the correlation parameter of a binormal distribution. The four estimators are then compared on two pattern recognition tasks: people identification through face recognition and book identification from the cover image. The similarity measures based on the L 1 norm prove to be less sensitive to noise and provide better performance than those based on L 2 norm . Keywords: template matching, robust statistics, correlation, face recognition, book recognition. 1. Introduction The estimation of similarity of patterns is a common lowlevel vision task which must be routinely performed by many computer vision systems. The Pear...
Modeling Country Risk Ratings Using Partial Orders
 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. RESEARCH
, 2004
"... In order to evaluate the creditworthiness of various countries, a learning model is induced from the 1998 S&P country risk ratings, using the 1998 values of nine economic and three political indicators. This learning model allows the construction of a partially ordered set describing the relative s ..."
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Cited by 8 (7 self)
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In order to evaluate the creditworthiness of various countries, a learning model is induced from the 1998 S&P country risk ratings, using the 1998 values of nine economic and three political indicators. This learning model allows the construction of a partially ordered set describing the relative superiority of countries on the basis of their creditworthiness, and it is shown that the Condorcet linear extensions of this poset match closely the S&P ratings. Moreover, the ratings derived from the model correlate highly with those of other rating agencies. The model is shown to provide excellent ratings even when applied to the following years ’ data or to the ratings of previously unrated countries. Rating changes implemented by S&P in subsequent years resolved most of the (few) discrepancies between the constructed poset and S&P’s initial ratings.
Network inference from traceroute measurements: Internet topology ‘species’,”arXiv:cs.NI/0510007
 Phys. Rev. E
, 2005
"... Internet mapping projects generally consist in sampling the network from a limited set of sources by usingtraceroute probes. This methodology, akin to the merging of spanning trees from the different sources to a set of destinations, leads necessarily to a partial, incomplete map of the Internet. Ac ..."
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Cited by 7 (0 self)
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Internet mapping projects generally consist in sampling the network from a limited set of sources by usingtraceroute probes. This methodology, akin to the merging of spanning trees from the different sources to a set of destinations, leads necessarily to a partial, incomplete map of the Internet. Accordingly, determination of Internet topology characteristics from such sampled maps is in part a problem of statistical inference. Our contribution begins with the observation that the inference of many of the most basic topological quantities – including network size and degree characteristics – fromtraceroute measurements is in fact a version of the socalled ‘species problem ’ in statistics. This observation has important implications, since species problems are often quite challenging. We focus here on the most fundamental example of atraceroute internet species: the number of nodes in a network. Specifically,
Indirect Inference for Dynamic Panel Models
, 2007
"... It is wellknown that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size(T) and large cross section sample size(N) asymptotics. The estimation bias is particularly relevant in practic ..."
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Cited by 7 (4 self)
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It is wellknown that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size(T) and large cross section sample size(N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The present paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes efficiency. The method is implemented in linear dynamic panel models with and without an incidental trend, but has wider applicability and can, for instance, be easily extended to more complicated frameworks such as nonlinear models. Monte Carlo studies show that the proposed procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and biascorrected ML estimators previously proposed in the literature and is shown to have superior finite sample properties to GMM and the biascorrected ML of Hahn and Kuersteiner (2002). Finite sample performance is compared with that of a recent estimator proposed by Han and Phillips (2007).
Empirical Edgeworth Expansions For Symmetric Statistics
, 1998
"... this paper the validity of a oneterm Edgeworth expansion for Studentized symmetric statistics is proved. We propose jackknife estimates for the unknown constants appearing in the expansion and prove their consistency. As a result we obtain the secondorder correctness of the empirical Edgeworth exp ..."
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Cited by 5 (1 self)
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this paper the validity of a oneterm Edgeworth expansion for Studentized symmetric statistics is proved. We propose jackknife estimates for the unknown constants appearing in the expansion and prove their consistency. As a result we obtain the secondorder correctness of the empirical Edgeworth expansion for a very general class of statistics, including Ustatistics, Lstatistics and smooth functions of the sample mean. We illustrate the application of the bootstrap in the case of a Ustatistic of degree two. 1. Introduction
Bias correction of OLSE in the regression model with lagged dependent variables
, 2000
"... It is well known that the ordinary leastsquares estimates (OLSE) of autoregressive models are biased in small sample. In this paper, an attempt is made to obtain the unbiased estimates in the sense of median or mean. Using Monte Carlo simulation techniques, we extend the medianunbiased estimator p ..."
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Cited by 3 (1 self)
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It is well known that the ordinary leastsquares estimates (OLSE) of autoregressive models are biased in small sample. In this paper, an attempt is made to obtain the unbiased estimates in the sense of median or mean. Using Monte Carlo simulation techniques, we extend the medianunbiased estimator proposed by Andrews (1993, Econometrica 61 (1), 139165) to the higherorder autoregressive processes, the nonnormal error term and inclusion of any exogenous variables. Also, we introduce the meanunbiased estimator, which is compared with OLSE and the mediumunbiased estimator. Some simulation studies are performed to examine whether the proposed estimation procedure works well or not, where AR(p) for p =1; 2; 3 models are examined. We obtain the results that it is possible to recover the true parameter values from OLSE and that the proposed procedure gives us the lessbiased estimators than OLSE. Finally, using actually obtained data, an empirical example of the median and meanunbiased estimators are shown. c