Use of pooled time series in the study of naturally occurring clinical events and problem behavior in a foster care setting.
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Pooled time series is an underused analytic technique with the potential to increase researchers' ability to exploit clinical data. This article demonstrates the value of pooled time series by analyzing the behavior of youths in a specialized foster care treatment setting in response to a naturally occurring clinical event: changes in the number of youths living together in a treatment foster care setting. Pooled time series moves beyond typical clinical analyses with an increased capability of controlling statistically for complex within-subject effects and with a clinically useful measure of effect size. The complexity of the intrasubject data made it virtually impossible to determine the relevant significance (i.e., clinical meaning) of the clinical event by the use of standard n = 1 visual analysis procedures or standard statistical methods (e.g., chi square). After things such as autocorrelation and individual time trends were statistically controlled, each additional youth increased the number of problematic behaviors by one behavior per youth per day on the Parent Daily Report.Autocorrelation pitch period detection is a key technique in voice signal process.Efficiency of the algorithm is also important except for the quality of the signal process.Pitch period of the dull sound is obtained through the short-time autocorrelation function,and polarity correlation and peak value estimation are used to raise the efficiency in the process of the autocorrelation.
Autocorrelation technique
Pitch detection algorithm
SIGNAL (programming language)
Autocorrelation matrix
Sound Quality
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In this study a new mathematical expression to describe the observed meandering autocorrelation functions in low-wind speed is proposed. The analysis utilizes a large number of best fit curves to show that the proposed theoretical function well reproduces the general form and the negative lobes characterizing the experimental meandering autocorrelation function. Further, the good agreement of the measured autocorrelation curves with the proposed algebraic autocorrelation function allows to calculate the magnitudes of the meandering period and of the loop parameter. The results agree with the values presented and discussed in the literature. Therefore, the new formulation describing experimental meandering autocorrelation functions can be used to simulate the dispersion of contaminant during low wind episodes and to determine relevant meandering parameters.
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A procedure involving autocorrelation is described which, for long averaging time and incoming signals with continuous spectrum, has a detection threshold equal to that of a corresponding conventional system consisting of band pass filter, rectifier, and low pass filter. The autocorrelation procedure is quite complex; a full autocorrelation function is computed and then subjected to a ``filtering'' operation. A simpler procedure, such as computing autocorrelation coefficients for only one or a few delay times, will in general be less effective. A third process, involving multiplication by a local sinusoidal signal, is also discussed and shown to be closely parallel to the conventional system.
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Autocorrelation matrix
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The standard calculation of the autocorrelation function (acf) assumes stationarity. This implies that the series does not contain trends or cycles. Nevertheless, computer programs are used to calculate acf's for any series, irrespective of whether the stationarity condition is satisfied or not. It has long been observed that the acf of a series that contains a trend shows a slowly declining pattern and that the acf of a series that contains cycles displays periodic behaviour. This is explained here. Equations are given for the autocorrelation function of a series that contains a deterministic component. The case of trends and cycles is examined in detail.
Autocorrelation technique
Component (thermodynamics)
Partial autocorrelation function
Moving-average model
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In a recent communication, Balza, Fromageot and Maniere described how to generate 4-level maximum-length sequences, and listed several of the properties of these sequences. Some of the expressions for the autocorrelation function are incorrect, and it is the purpose of this letter to give the correct formulas, and to show how to reduce the off-peak autocorrelation by choosing different analogue levels to correspond with the levels of the sequence.
Autocorrelation technique
Sequence (biology)
Autocorrelation matrix
Complementary sequences
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Sample (material)
Autocorrelation technique
Stationary process
Spike train
Autocorrelation matrix
Moving-average model
Partial autocorrelation function
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Time series are demeaned when sample autocorrelation functions are computed. By the same logic it would seem appealing to remove seasonal means from seasonal time series before computing sample autocorrelation functions. Yet, standard practice is only to remove the overall mean and ignore the possibility of seasonal mean shifts in the data. Whether or not time series are seasonally demeaned has very important consequences on the asymptotic behavior of autocorrelation functions. The effect on the asymptotic distribution of seasonal mean shifts and their removal is investigated and the practical consequences of these theoretical developments are discussed. We also examine the small sample behavior of autocorrelation function estimates through Monte Carlo simulations.
Partial autocorrelation function
Autocorrelation technique
Sample (material)
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This paper presents a fundamental frequency estimation algorithm of noisy speech signal using the combination of windowless and normalized autocorrelation functions. Instead of the input speech signal, we employ its windowless autocorrelation function for obtaining the normalized autocorrelation function. The windowless autocorrelation function is a noise-reduced version of the input speech signal where the periodicity is more apparent with enhanced pitch peak. Experimental results on male and female voices in white noise indicate that the proposed method sufficiently outperforms existing methods in terms of gross pitch error.
Autocorrelation technique
Pitch detection algorithm
Autocorrelation matrix
SIGNAL (programming language)
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A study of autocorrelation properties in pneumatic transport systems has been performed. The autocorrelation height at zero time delay and the time delay at which the autocorrelation function becomes zero have been considered.
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Autocorrelation matrix
Zero (linguistics)
Partial autocorrelation function
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The use of autocorrelation functions and correlation lengths in surface texture investigation is considered. Approximate methods for evaluating correlation functions using random-process analysis are considered. Experimental data are also given. In particular, some important properties of the surface texture parameters, S m and S, used in the approximate evaluation of the autocorrelation function, are considered.
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Texture (cosmology)
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