An Improved Measure for Lack of Fit in Time Series Models

Fisher, Thomas J.; Robbins, Michael W.

doi:10.5705/ss.202016.0286

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…where the (L + 1 − i)/(L + 1) terms terms present a weighting scheme that decreases as the lag increases. The statistic is an adaptation of that found in Fisher and Gallagher (2012), Robbins and Fisher (2015) and Fisher and Robbins (2018).…”

Section: Weighted Statisticsmentioning

confidence: 99%

On testing for the equality of autocovariance in time series

Cirkovic

Fisher

2021

Environmetrics

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The comparison of two time series often arises in climatology, environmental science, and econometrics. Through natural and physical circumstances these series are often dependent. We develop a hypothesis test for the equality of autocovariance functions for two linearly dependent multivariate time series. Previous tests for two independent series are reviewed and extended to the dependent case. A univariate bootstrapped statistic that automatically selects the order of the test is extended to the multivariate setting as well. The performance of the tests are compared through simulation and the methods are applied to univariate temperature and multivariate air quality series. Empirical results show that by accounting for the correlation between series substantial improvements in power can be made in the detection of differences in the autocovariance.

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Section: Weighted Statisticsmentioning

confidence: 99%

On testing for the equality of autocovariance in time series

Cirkovic

Fisher

2021

Environmetrics

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Using the Methods of Statistical Data Analysis to Improve the Trustworthiness of Software Reliability Modeling

2022

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In the past, many Software Reliability Growth Models (SRGMs) have been proposed to predict various aspects of software reliability and NHPP (Non-Homogeneous Poisson Process) based SRGMs have drawn a lot of attention. But it should be noted that, in practice, blindly applying SRGMs might not lead to meaningful results when the characteristics of data (i.e., data type) don't fit the characteristics of selected SRGM(s). Also, there does not exist any guidelines that project managers or developers can follow to select the most appropriate SRGMs for a particular failure data set. On the other hand, waiting to collect a substantial amount of failure data before being able to fit SRGM(s) might not be feasible in many cases. In this paper, we propose to use the methods of statistical data analysis to investigate the characteristics of software failure data and to improve the accuracy of software reliability growth modeling. Here SRGMs will be broadly partitioned into two categories: the "NHPP models" and the "not belonging to the class of NHPP models". Similarly, software failure data can be classified into two categories, the "NHPP-type data" and the "not NHPP-type data". We will propose a two-phase method to verify the hypothesis that failure data follows an NHPP. Through the proposed two-phase test and the Laplace trend test, we will be able to infer whether a dataset is the NHPP-type and whether it is properly used by NHPP-based SRGMs. In this case, the final decision of choosing the most suitable NHPP model(s) to model the software failure process would be made, and the software reliability prediction result is trustworthy and helpful after we can correctly judge whether the dataset is said to be absolutely NHPP-type or not. Experiments based on 25 real software failure data are presented and analyzed. Using our proposed method, project managers and/or developers will be able to analyze and use collected failure data to help improve both planning accuracy and software quality.

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An Improved Measure for Lack of Fit in Time Series Models

Cited by 2 publications

References 43 publications

On testing for the equality of autocovariance in time series

On testing for the equality of autocovariance in time series

Using the Methods of Statistical Data Analysis to Improve the Trustworthiness of Software Reliability Modeling

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