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SelfSimilarity in World Wide Web Traffic: Evidence and Possible Causes
, 1996
"... Recently the notion of selfsimilarity has been shown to apply to widearea and localarea network traffic. In this paper we examine the mechanisms that give rise to the selfsimilarity of network traffic. We present a hypothesized explanation for the possible selfsimilarity of traffic by using a p ..."
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Cited by 1414 (28 self)
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Recently the notion of selfsimilarity has been shown to apply to widearea and localarea network traffic. In this paper we examine the mechanisms that give rise to the selfsimilarity of network traffic. We present a hypothesized explanation for the possible selfsimilarity of traffic by using a particular subset of wide area traffic: traffic due to the World Wide Web (WWW). Using an extensive set of traces of actual user executions of NCSA Mosaic, reflecting over half a million requests for WWW documents, we examine the dependence structure of WWW traffic. While our measurements are not conclusive, we show evidence that WWW traffic exhibits behavior that is consistent with selfsimilar traffic models. Then we show that the selfsimilarity insuch traffic can be explained based on the underlying distributions of WWW document sizes, the effects of caching and user preference in le transfer, the effect of user "think time", and the superimposition of many such transfers in a local area network. To do this we rely on empirically measured distributions both from our traces and from data independently collected at over thirty WWW sites.
Model Selection and the Principle of Minimum Description Length
 Journal of the American Statistical Association
, 1998
"... This paper reviews the principle of Minimum Description Length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework discriminates between competing models based on the complexity of each description. This ..."
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Cited by 195 (8 self)
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This paper reviews the principle of Minimum Description Length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework discriminates between competing models based on the complexity of each description. This approach began with Kolmogorov's theory of algorithmic complexity, matured in the literature on information theory, and has recently received renewed interest within the statistics community. In the pages that follow, we review both the practical as well as the theoretical aspects of MDL as a tool for model selection, emphasizing the rich connections between information theory and statistics. At the boundary between these two disciplines, we find many interesting interpretations of popular frequentist and Bayesian procedures. As we will see, MDL provides an objective umbrella under which rather disparate approaches to statistical modeling can coexist and be compared. We illustrate th...
A Monte Carlo approach to nonnormal and nonlinear statespace modeling
 Journal of the American Statistical Association
, 1992
"... ..."
Estimators for LongRange Dependence: An Empirical Study
, 1995
"... Various methods for estimating the selfsimilarity parameter and/or the intensity of longrange dependence in a time series are available. Some are more reliable than others. To discover the ones that work best, we apply the different methods to simulated sequences of fractional Gaussian noise and f ..."
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Cited by 155 (5 self)
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Various methods for estimating the selfsimilarity parameter and/or the intensity of longrange dependence in a time series are available. Some are more reliable than others. To discover the ones that work best, we apply the different methods to simulated sequences of fractional Gaussian noise and fractional ARIMA(0, d, 0). We also provide here a theoretical justification for the method of residuals of regression.
Parallel and Distributed Simulation of Discrete Event Systems
, 1995
"... The achievements attained in accelerating the simulation of the dynamics of complex discrete event systems using parallel or distributed multiprocessing environments are comprehensively presented. While parallel discrete event simulation (DES) governs the evolution of the system over simulated time ..."
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Cited by 118 (17 self)
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The achievements attained in accelerating the simulation of the dynamics of complex discrete event systems using parallel or distributed multiprocessing environments are comprehensively presented. While parallel discrete event simulation (DES) governs the evolution of the system over simulated time in an iterative SIMD way, distributed DES tries to spatially decompose the event structure underlying the system, and executes event occurrences in spatial subregions by logical processes (LPs) usually assigned to different (physical) processing elements. Synchronization protocols are necessary in this approach to avoid timing inconsistencies and to guarantee the preservation of event causalities across LPs. Included in the survey are discussions on the sources and levels of parallelism, synchronous vs. asynchronous simulation and principles of LP simulation. In the context of conservative LP simulation (Chandy/Misra/Bryant) deadlock avoidance and deadlock detection/recovery strategies, Con...
Bootstraps for Time Series
, 1999
"... We compare and review block, sieve and local bootstraps for time series and thereby illuminate theoretical facts as well as performance on nitesample data. Our (re) view is selective with the intention to get a new and fair picture about some particular aspects of bootstrapping time series. The ge ..."
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Cited by 112 (4 self)
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We compare and review block, sieve and local bootstraps for time series and thereby illuminate theoretical facts as well as performance on nitesample data. Our (re) view is selective with the intention to get a new and fair picture about some particular aspects of bootstrapping time series. The generality of the block bootstrap is contrasted by sieve bootstraps. We discuss implementational dis/advantages and argue that two types of sieves outperform the block method, each of them in its own important niche, namely linear and categorical processes, respectively. Local bootstraps, designed for nonparametric smoothing problems, are easy to use and implement but exhibit in some cases low performance. Key words and phrases. Autoregression, block bootstrap, categorical time series, context algorithm, double bootstrap, linear process, local bootstrap, Markov chain, sieve bootstrap, stationary process. 1 Introduction Bootstrapping can be viewed as simulating a statistic or statistical pro...
Convergence rates of posterior distributions
 Ann. Statist
, 2000
"... We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinitedimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, logspline models, D ..."
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Cited by 106 (14 self)
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We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinitedimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, logspline models, Dirichlet processes and interval censoring. 1. Introduction. Suppose
Classes of kernels for machine learning: a statistics perspective
 Journal of Machine Learning Research
, 2001
"... In this paper, we present classes of kernels for machine learning from a statistics perspective. Indeed, kernels are positive definite functions and thus also covariances. After discussing key properties of kernels, as well as a new formula to construct kernels, we present several important classes ..."
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Cited by 103 (2 self)
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In this paper, we present classes of kernels for machine learning from a statistics perspective. Indeed, kernels are positive definite functions and thus also covariances. After discussing key properties of kernels, as well as a new formula to construct kernels, we present several important classes of kernels: anisotropic stationary kernels, isotropic stationary kernels, compactly supportedkernels, locally stationary kernels, nonstationary kernels, andseparable nonstationary kernels. Compactly supportedkernels andseparable nonstationary kernels are of prime interest because they provide a computational reduction for kernelbased methods. We describe the spectral representation of the various classes of kernels and conclude with a discussion on the characterization of nonlinear maps that reduce nonstationary kernels to either stationarity or local stationarity.
A linear nongaussian acyclic model for causal discovery
 J. Machine Learning Research
, 2006
"... In recent years, several methods have been proposed for the discovery of causal structure from nonexperimental data. Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to ..."
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Cited by 101 (28 self)
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In recent years, several methods have been proposed for the discovery of causal structure from nonexperimental data. Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuousvalued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have nonGaussian distributions of nonzero variances. The solution relies on the use of the statistical method known as independent component analysis, and does not require any prespecified timeordering of the variables. We provide a complete Matlab package for performing this LiNGAM analysis (short for Linear NonGaussian Acyclic Model), and demonstrate the effectiveness of the method using artificially generated data and realworld data.