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34
Supporting Contentbased Searches on Time Series via Approximation
 International Conference on Scientific and Statistical Database Management
, 2000
"... Fast retrieval of time series in terms of their contents is important in many application domains. This paper studies database techniques supporting fast searches for time series whose contents are similar to what users specify. The content types studied include shapes, trends, cyclic components, au ..."
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Cited by 36 (3 self)
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Fast retrieval of time series in terms of their contents is important in many application domains. This paper studies database techniques supporting fast searches for time series whose contents are similar to what users specify. The content types studied include shapes, trends, cyclic components, autocorrelation functions and partial autocorrelation functions. Due to the complex nature of the similarity searches involving such contents, traditional database techniques usually cannot provide a fast response when the involved data volume is high. This paper hence proposes to answer such contentbased queries using appropriate approximation techniques. The paper then introduces two specific approximation methods, one is wavelet based and the other linefitting based. Finally, the paper reports some experiments conducted on a stock price data set as well as a synthesized random walk data set, and shows that both approximation methods significantly reduce the query processing time without introducing intolerable errors.
Test of significance when data are curves
 Journal of the American Statistical Association
, 1998
"... With modern technology, massive data can easily be collected in a form of multiple sets of curves. New statistical challenge includes testing whether there is any statistically significant difference among these sets of curves. In this paper, we propose some new tests for comparing two groups of cur ..."
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Cited by 32 (1 self)
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With modern technology, massive data can easily be collected in a form of multiple sets of curves. New statistical challenge includes testing whether there is any statistically significant difference among these sets of curves. In this paper, we propose some new tests for comparing two groups of curves based on the adaptive Neyman test and the wavelet thresholding techniques introduced in Fan (1996). We demonstrate that these tests inherit the properties outlined in Fan (1996) and they are simple and powerful for detecting di erences between two sets of curves. We then further generalize the idea to compare multiple sets of curves, resulting in an adaptive highdimensional analysis of variance, called HANOVA. These newly developed techniques are illustrated by using a dataset on pizza commercial where observations are curves and an analysis of cornea topography in ophthalmology where images of individuals are observed. A simulation example is also presented to illustrate the power of the adaptive Neyman test.
Regression And Time Series Model Selection Using Variants Of The Schwarz Information Criterion
, 1997
"... The Schwarz (1978) information criterion, SIC, is a widelyused tool in model selection, largely due to its computational simplicity and effective performance in many modeling frameworks. The derivation of SIC (Schwarz, 1978) establishes the criterion as an asymptotic approximation to a transformati ..."
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Cited by 16 (1 self)
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The Schwarz (1978) information criterion, SIC, is a widelyused tool in model selection, largely due to its computational simplicity and effective performance in many modeling frameworks. The derivation of SIC (Schwarz, 1978) establishes the criterion as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. In this paper, we investigate the derivation for the identification of terms which are discarded as being asymptotically negligible, but which may be significant in small to moderate samplesize applications. We suggest several SIC variants based on the inclusion of these terms. The results of a simulation study show that the variants improve upon the performance of SIC in two important areas of application: multiple linear regression and time series analysis. 1. Introduction One of the most important problems confronting an investigator in statistical modeling is the choice of an appropriate model to characterize the underlyin...
Are Correlations of Stock Returns Justified by Subsequent Changes in National Outputs
 Journal of International Money and Finance
, 2003
"... In an integrated world capital market, the same pricing kernel is applicable to all securities. We apply this idea to the stock returns of different countries. We investigate the underlying determinants of crosscountry stock return correlations. First, we determine, for a given, measured degree of ..."
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Cited by 14 (2 self)
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In an integrated world capital market, the same pricing kernel is applicable to all securities. We apply this idea to the stock returns of different countries. We investigate the underlying determinants of crosscountry stock return correlations. First, we determine, for a given, measured degree of commonality of country outputs, what should be the degree of correlation of national stock returns. We propose a framework that contains a statistical model for output and an intertemporal financial market model for stock returns. We then attempt to match the correlations generated by the model with measured correlations. Our results show that under the hypothesis of market segmentation, the model correlations are much smaller than observed correlations. However, assuming world markets are integrated, our model correlations closely match observed correlations. ∗Dumas and Ruiz acknowledge gratefully the support of the HEC Foundation. Ruiz acknowledges also gratefully the support of IFM2. Dumas is also affiliated with the University of Pennsylvania (as an Adjunct Professor), the
Statistical Estimation in VaryingCoefficient Models
"... Varyingcoefficient models are a useful extension of classical linear models. They arise naturally when one wishes to examine how regression coefficients change over different groups characterized by certain covariates such as age. The appeal of these models is that the coefficient functions can ea ..."
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Cited by 11 (3 self)
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Varyingcoefficient models are a useful extension of classical linear models. They arise naturally when one wishes to examine how regression coefficients change over different groups characterized by certain covariates such as age. The appeal of these models is that the coefficient functions can easily be estimated via a simple local regression. This yields a simple onestep estimation procedure. We show that such a onestep method can not be optimal when different coefficient functions admit different degrees of smoothness. This drawback can be repaired by using our proposed twostep estimation procedure. The asymptotic meansquared error for the twostep procedure is obtained and is shown to achieve the optimal rate of convergence. A few simulation studies show that the gain by the twostep procedure can be quite substantial. The methodology is illustrated by an application to an environmental dataset.
A Bootstrap Variant of AIC for StateSpace Model Selection
 STATISTICA SINICA
, 1997
"... Following in the recent work of Hurvich and Tsai (1989, 1991, 1993) and Hurvich, Shumway, and Tsai (1990), we propose a corrected variant of AIC developed for the purpose of smallsample statespace model selection. Our variant of AIC utilizes bootstrapping in the statespace framework (Stoffer and ..."
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Cited by 9 (4 self)
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Following in the recent work of Hurvich and Tsai (1989, 1991, 1993) and Hurvich, Shumway, and Tsai (1990), we propose a corrected variant of AIC developed for the purpose of smallsample statespace model selection. Our variant of AIC utilizes bootstrapping in the statespace framework (Stoffer and Wall (1991)) to provide an estimate of the expected KullbackLeibler discrepancy between the model generating the data and a fitted approximating model. We present simulation results which demonstrate that in smallsample settings, our criterion estimates the expected discrepancy with less bias than traditional AIC and certain other competitors. As a result, our AIC variant serves as an effective tool for selecting a model of appropriate dimension. We present an asymptotic justification for our criterion in the Appendix.
Origins and scale dependence of temporal variability in the transparency of Lake Tahoe
, 1999
"... Secchi depth has been measured in Lake Tahoe an average of every 12 d since July 1967. Because of the unusual clarity of the lake, Secchi depth measurement is responsive to small changes in lightattenuating particles, and the record exhibits strong variability at the seasonal, interannual, and deca ..."
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Cited by 9 (3 self)
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Secchi depth has been measured in Lake Tahoe an average of every 12 d since July 1967. Because of the unusual clarity of the lake, Secchi depth measurement is responsive to small changes in lightattenuating particles, and the record exhibits strong variability at the seasonal, interannual, and decadal scales. Using recently developed methods of applied timeseries analysis, the mechanisms of change were delineated at each scale. The seasonal pattern is a bimodal one, with two minima at approximately June and December. The June minimum is due mostly to cumulative discharge of suspended sediments following melting of the snowpack. The December minimum is probably a result of mixedlayer deepening as the thermocline passes through layers of phytoplankton and other lightattenuating particles that reach a maximum below the summer mixed layer. The interannual scale exhibits two modes of variability, one during the weakly stratified autumn–winter period and the other during the more stratified spring– summer period. The first mode is a result of variable depth of mixing in this unusually deep lake, while the second results from yeartoyear changes in spring runoff. A decadal trend also exists (�0.25 m yr�1), resulting from accumulation of materials in the water column. It is not yet understood, however, how much of this change is due to phytoplankton or recent phytoplanktonderived materials and how much is due to other materials such as mineral suspensoids. Based on the available measurements and physical considerations, both categories may play a significant
WaveletBased Combined Signal Filtering and Prediction
"... Abstract — We survey a number of applications of the wavelet transform in time series prediction. We show how multiresolution prediction can capture shortrange and longterm dependencies with only a few parameters to be estimated. We then develop a new multiresolution methodology for combined noise ..."
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Cited by 7 (0 self)
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Abstract — We survey a number of applications of the wavelet transform in time series prediction. We show how multiresolution prediction can capture shortrange and longterm dependencies with only a few parameters to be estimated. We then develop a new multiresolution methodology for combined noise filtering and prediction, based on an approach which is similar to the Kalman filter. Based on considerable experimental assessment, we demonstrate the powerfulness of this methodology. Index Terms — Wavelet transform, filtering, forecasting, resolution, scale, autoregression, time series, model,
RTSPC: A Software Utility for RealTime SPC and Tool Data Analysis
"... Competition in the semiconductor industry is forcing manufacturers to continuously improve the capability of their equipment. The analysis of realtime sensor data from semiconductor manufacturing equipment presents the opportunity to reduce the cost of ownership of the equipment. Previous work by t ..."
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Cited by 5 (2 self)
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Competition in the semiconductor industry is forcing manufacturers to continuously improve the capability of their equipment. The analysis of realtime sensor data from semiconductor manufacturing equipment presents the opportunity to reduce the cost of ownership of the equipment. Previous work by the authors showed that time series filtering in combination with multivariate analysis techniques can be utilized to perform statistical process control, and thereby generate realtime alarms in the case of equipment malfunction. A more robust version of this fault detection algorithm is presented. The algorithm is implemented through RTSPC, a software utility which collects realtime sensor data from the equipment and generates realtime alarms. Examples of alarm generation using RTSPC on a plasma etcher are presented. Lee, et al 3 RTSPC: A Utility For Realtime SPC 1.0 Introduction To compete in today's semiconductor industry, companies must continuously improve upon their manufacturing...
Generalizing The Derivation Of The Schwarz Information Criterion
, 1999
"... The Schwarz information criterion (SIC, BIC, SBC) is one of the most widely known and used tools in statistical model selection. The criterion was derived by Schwarz (1978) to serve as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. Althoug ..."
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Cited by 4 (1 self)
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The Schwarz information criterion (SIC, BIC, SBC) is one of the most widely known and used tools in statistical model selection. The criterion was derived by Schwarz (1978) to serve as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. Although the original derivation assumes that the observed data is independent, identically distributed, and arising from a probability distribution in the regular exponential family, SIC has traditionally been used in a much larger scope of model selection problems. To better justify the widespread applicability of SIC, we derive the criterion in a very general framework: one which does not assume any specific form for the likelihood function, but only requires that it satisfies certain nonrestrictive regularity conditions.