One-Sided Confidence Contours for Probability Distribution Functions

Birnbaum, Z. W.; Tingey, F.H.

doi:10.1214/aoms/1177729550

Cited by 167 publications

(71 citation statements)

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“…The Birnbaum-Tingey onesided contour analysis was used for the one-sided Kolmogorov-Smirnov test. It takes variance into account and can indicate if the CDF of one set of samples is larger than the CDF of the other set [9], [37]. It also returns a p-value for the assertion.…”

Section: Statistical Analysis Of Resultsmentioning

confidence: 99%

Reducing Features to Improve Code Change-Based Bug Prediction

Shivaji

Whitehead

Akella

et al. 2013

IIEEE Trans. Software Eng.

220

123

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Abstract-Machine learning classifiers have recently emerged as a way to predict the introduction of bugs in changes made to source code files. The classifier is first trained on software history, and then used to predict if an impending change causes a bug. Drawbacks of existing classifier-based bug prediction techniques are insufficient performance for practical use and slow prediction times due to a large number of machine learned features. This paper investigates multiple feature selection techniques that are generally applicable to classification-based bug prediction methods. The techniques discard less important features until optimal classification performance is reached. The total number of features used for training is substantially reduced, often to less than 10 percent of the original. The performance of Naive Bayes and Support Vector Machine (SVM) classifiers when using this technique is characterized on 11 software projects. Naive Bayes using feature selection provides significant improvement in buggy F-measure (21 percent improvement) over prior change classification bug prediction results (by the second and fourth authors [28]). The SVM's improvement in buggy F-measure is 9 percent. Interestingly, an analysis of performance for varying numbers of features shows that strong performance is achieved at even 1 percent of the original number of features.

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Section: Statistical Analysis Of Resultsmentioning

confidence: 99%

Reducing Features to Improve Code Change-Based Bug Prediction

Shivaji

Whitehead

Akella

et al. 2013

IIEEE Trans. Software Eng.

220

123

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“…We can also perform formal goodness of fit test in order to verify which distribution fits to the data. We apply the Kolmogorov-Smirnov test (KS test) [18] and Anderson-Darling test (AD test) [19] for the goodness of fit purpose. Table 2 lists the MLE estimates of the parameters β and θ and values of the test statistics which are KS test and AD test.…”

Section: Discussionmentioning

confidence: 99%

The Length-Biased Exponentiated Inverted Weibull Distribution

Seenoi¹,

Supapakorn²,

Bodhisuwan³

2014

Int. J. of Pure and Appl. Math.

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This paper develops a length-biased of the exponentiated inverted Weibull (EIW) distribution, it is named as the length-biased exponentiated inverted Weibull (LBEIW). Some probability functions are derived, including the moments of this distribution. The length-biased inverted Weibull (LBIW) distribution is presented as a special case of the LBEIW distribution. Maximum likelihood estimation method is employed to parameter estimation. Finally, a real dataset of distance between cracks in a pipe is used to illustrate by fitting the LBEIW, EIW, LBIW and Weibull distributions to the data. It is shown that the LBEIW and EIW distributions can be fitted well.

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“…We used a t-test in order to ensure that the two density functions are different and the resulted p-value was < 1%. Because of the fact that the two pdfs are not Gaussian, we have also applied the Kolmogorov-Smirnov test or KS-test [3] that does not require normal pdfs. The Kolmogorov-Smirnov test indicated that, indeed, the normal and abnormal samples come from different pdfs (p-value = 2.09e-07).…”

Section: Offline Experimentsmentioning

confidence: 99%