In our previous work, we proposed wavelet shrinkage estimation (WSE) for nonhomogeneous Poisson process (NHPP)-based\r\nsoftware reliability models (SRMs), where WSE is a data-transform-based nonparametric estimation method. Among many\r\nvariance-stabilizing data transformations, the Anscombe transform and the Fisz transform were employed. We have shown that\r\nit could provide higher goodness-of-fit performance than the conventional maximum likelihood estimation (MLE) and the\r\nleast squares estimation (LSE) in many cases, in spite of its non-parametric nature, through numerical experiments with real\r\nsoftware-fault count data.With the aim of improving the estimation accuracy ofWSE, in this paper we introduce other three data\r\ntransformations to preprocess the software-fault count data and investigate the influence of different data transformations to the\r\nestimation accuracy ofWSE through goodness-of-fit test
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