R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc., 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....weekend. On average, NYUHome received 1706 requests an hour: Figure 4(b) shows the minimum and maximum requests received during the same hour over the two week period. Figure 4(c) shows the cumulative distribution of the inter request arrival interval. Using the # method as the goodness of fit [15] measure, we found that this distribution is captured very well by an Exponential distribution with # = 0.526, suggesting a Poisson arrival process. This observation seemingly conflicts with that from previous studies of web servers [6] and telnet sessions [33] where it was found that the ....

R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc, 1986.

....weekend. On average, NYUHome received 1706 requests an hour: Figure 4(b) shows the minimum and maximum requests received during the same hour over the two week period. Figure 4(c) shows the cumulative distribution of the inter request arrival interval. Using the X 2 method as the goodness of fit [15] measure, 3 we found that this distribution is captured very well by an Exponential distribution with ) 0.526, suggesting a Poisson arrival process. This observation seemingly conflicts with that from previous studies of web servers [6] and telnet sessions [33] where it was found that the ....

R.B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc, 1986.

....approximations of the data. Unfortunately, because of the transformation used to convert the fractions of samples to fractions of events, we cannot apply standard goodness of t hypothesis testing techniques such as the ChiSquare goodness of t test or Anderson Darling goodness of t test [8] to these distributions. However, to put these ts in perspective, several observations are worth noting. First, the maximum positive and negative di erences from the data to the tted lines are D = 029, D = 003, D UB = 007, and D UB = 031. Second, the maximum di erence between the ....

R. D'Agostino and M. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc., 1986.

....4. To develop a model for the object size distributions seen for different size limits, we use standard statistical methods similar to those used by Paxson et al. in [32] We used both Chi Square and Anderson Darling (A 2) empirical distribution functions (EDF) for estimation of goodness of fit [15]. By comparing with several distributions, such as Lognormal, Exponential, Weibull and Pareto, we found that the best 10 lOO lOOO 10000 lOO lOOO Objectsze Objectsze (a) cnn (b) yahoo 1.0 (c) nytimes 1 oo 1 ooo 1 oooo 1 oo 1 ooo Fig. 4. The cumulative distribution function (of ....

R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc, 1986.

....its model parameters through one sweep of the data at each resolution. The error metric on the mean modeler is a variant of the root mean square error (RMSE) Our second model captures the normality of systematic partitions of the data by utilizing the Anderson Darling goodness of fit test [5]. This model is called the goodness of fit modeler. Similar to the mean modeler, the goodness of fit modeler is able to calculate its model parameters through one sweep of the data. However, this modeler attempts to fit the data to a normal distribution. The error on this model is the Type I error ....

....deviation, i . For the goodness of fit modeler, the partitioning step stops when the hypothesis test for normality is not rejected. We use the Anderson Darling test for normality (which is considered to be the most powerful goodness of fit test for normality) for our goodness of fit test [5]. The Anderson Darling test involves calculating the A metric for variable v N( i , i ) which is defined to be ( n j j n z j z j n A = 1 1 ln( ln( 1 2 1 2 where n = number of data points for v i and z j = i j x ) is the standard ....

[Article contains additional citation context not shown here]

D'Agostino, R.B., and Stephens, M.A. Goodness-of-fit Techniques, Marcel Dekker, Inc., 1986.

....methods similar to those used by Paxson in [22] Due to space restrictions, we discuss only the results for the cnn trace, which is representative of the others. Comparing several distributions, such as Lognormal, Exponential, Weibull and Pareto, using the Chi Square method as the goodness of fit [12] measure, we found that object sizes were best modeled using an Exponential distribution (with CDF F (x) 1 e #x ) The results of the Chi Square tests were in the range 0.1 to 2.5, showing a very close fit. Although not shown, we observed similar distribution fits for different settings of ....

R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc, 1986.

....weekend. On average, NYUHome received 1706 requests an hour: Figure 4(b) shows the minimum and maximum requests received during the same hour over the two week period. Figure 4(c) shows the cumulative distribution of the interrequest arrival interval. Using the # method as the goodness of fit [15] measure, we found that this distribution is captured very well by an Exponential distribution with # = 0.526, suggesting a Poisson arrival process. This observation seemingly conflicts with that from previous studies of web servers [6] and telnet sessions [33] where it was found that the ....

R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc, 1986.

.... Parametric techniques assume that knowledge about the underlying statistical distribution is available (often normal distribution is assumed) and that the task is to validate the assumption regarding the distribution, calculate the corresponding parameters, and establish intervals of confidence [D A86]. Nonparametric techniques do not make any assumptions about the statistical distribution. They aim to figure conceptually and quantitatively the simplest (and therefore best) model which fits the recorded data [Can99] Therefore, nonparametric techniques are significantly more computationally ....

R.B. D'Agostino, M. A. Stephens. "Goodness-of-fit techniques." New York: M. Dekker, 1986.

....reciprocal of the mean. A statistical test is that of Anderson Darling [6] This test has been found to be more powerful than either the Kolmogorov Smirnov or the # tests, i.e. its probability of correctly 136 rejecting the null hypothesis (that the distribution is exponential) is greater; see [15]. This is, in part, due to the fact that the Anderson Darling test employs the full empirical distribution (rather than binning, as in a # test) allowing it to give more weight to larger sample values whose presence can lead to a violation of the null hypothesis. For a set of n rank ordered ....

....is: A n (2i 1) log(1 e t i t t n 1 i t o where t = n 1 i=1 t i is the empirical mean inter event time. We reject the null hypothesis at significance level # if the test statistic exceeds the tabulated values appropriate for that level; see, e.g. Table 4. 11 in [15]. We note the importance of using the table appropriate to the present case in which the mean is estimated from the sample, rather than being specified in advance. Moreover, the table explicitly takes into account the e#ect of a finite sample size n. ....

R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, New York, 1986.

....approximations of the data. Unfortunately, because of the transformation used to convert the fractions of samples to fractions of events, we cannot apply standard goodness of fit hypothesis testing techniques such as the Chi Square goodness of fit test or Anderson Darling goodness of fit test [8] to these distributions. How ever, to put these fits in perspective, several observations are worth noting. First, the maximum positive and negative differences from the data to the fitted lines are Dr, s .029, DZs .003, Dv s = 007, and Ds = 031. Second, the maximum difference between the ....

R. D'Agostino and M. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc., 1986.

....line. The Q Q plots in Figures 4 and 5 compare the log of the rate distribution to the normal distribution for two of the traces (Access1c and Regional2) The fit between the two is visually good. As in Reference [2] we further assess the goodness of fit using the Shapiro Wilk normality test [5]. For Access1c (Figure 4) we can not reject the null hypothesis that the log of rate comes from normal distribution at 25 significance level; for Regional2 (Figure 5) we can not reject normality at any level of significance. This suggests the fit for a normal distribution is indeed very good. ....

R. D'Agostino and M. Stephens, Eds., "Goodness-of-Fit Techniques," Marcel Dekker, New York, 1986.

....of data transferred, continuous in the non negative integers. As such the values of the variables do not naturally fall into a finite number of categories, which makes using the well known chi squared test less than ideal because it requires somewhat arbitrary choices regarding binning [Knuth81, DS86] The goodness of fit test commonly used with continuous data is the Kolmogorov Smirnov test. The authors of [DS86] however, recommend the Anderson Darling ( test [AD54] instead. They state that is often much more powerful than either Kolmogorov Smirnov or chi squared, and that ....

....fall into a finite number of categories, which makes using the well known chi squared test less than ideal because it requires somewhat arbitrary choices regarding binning [Knuth81, DS86] The goodness of fit test commonly used with continuous data is the Kolmogorov Smirnov test. The authors of [DS86] however, recommend the Anderson Darling ( test [AD54] instead. They state that is often much more powerful than either Kolmogorov Smirnov or chi squared, and that particularly good for detecting deviations in the tails of a distribution, often the most important to detect. We ....

[Article contains additional citation context not shown here]

R. B. D'Agostino and M. A. Stephens, editors, "Goodness-of-Fit Techniques", Marcel Dekker, Inc., 1986.

....4. To develop a model for the object size distributions seen for different size limits, we use standard statistical methods similar to those used by Paxson et al. in [27] We use both Chi Square and Anderson Darling (A 2) empirical distribution functions (EDF) for estimation of goodness of fit [12]. We found that most of object size distributions have a very good fit to the Exponential distribution, whose cumulative distribution function is F(x) II1, 1 I , I . o Object size Object size Object size (a) cnn (b) yahoo (c) nytimes o I1=1 I I I= ....

R.B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc, 1986.

....behavior from Web client logs. To develop these models we use the client traces collected at Boston University in 1994 and 1995 by Cunha et al. 29] Our modeling methodology is similar to the techniques used by Paxson in [102] These techniques use goodness of fit tests described in detail in [22, 30, 109]. In this thesis, we present and use models extracted from the 94 and 95 client traces 1 . Workload generators are often used in a local area network when researchers do not have access to client systems deployed in the wide area. Use of workload generators in the local area means that effects ....

....models for each of these Web characteristics required the analysis of empirically measured Web workload traces. The most common way of specifying a statistical model for a set of data the is through the use of visual methods such as quantile quantile or cumulative distribution function (CDF) plots [30]. These methods, however, do not distinguish between two closely fitting distributions nor do they provide any level of confidence in the fit of the model. To address this drawback one can use goodness of fit tests [30, 102] However, these tests also present a number of problems. Methods which ....

[Article contains additional citation context not shown here]

R. D'Agostino and M. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc., 1986.

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R. B. D'Agostino and M. A. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc., 1986.

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M. A. Stephens, Goodness-of-fit techniques, New York : M. Dekker, 1986.

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R. B. D'Agostino and M. A. Stephens(editors), Goodness-of-fit Techniques. New York: Marcel Dekker, June, 1986, pp. 63-93, pp. 97-145, pp. 421-457.

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R. B. D'Agostino and M. A. Stephens. Goodness-Of-Fit Techniques. Marcel Dekker Inc., 1986.

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R. B. D'Agostino and M. A. Stephens (eds.), Goodness-of-Fit Techniques, 1986.

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R. D'Agostino and M. Stephens, editors, Goodness-of-Fit Techniques, Marcel Dekker, Inc., 1986.

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R. B. D'Agostino and M. A. Stephens, editors. Goodness of Fit Techniques. Marcel Dekker, 1986. (pp 68, 69)

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D' Agostino, R.B., and Stephens, M.A. Goodness-of-fit Techniques, Marcel Dekker, Inc., 1986.

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R.B. D'Agostino, M.A. Stephens, Goodness-of-Fit Techniques, Marcel Dekker, New York, 1986.

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D'Ag#U tino, R. and Stephens, M. (1986). Goodness-of-Fit Techniques. Marcel Dekker, New York.

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R. D'Agostino and M. Stephens, editors. Goodness-of-Fit Techniques. Marcel Dekker, Inc., 1986.

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