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We propose a first-order autoregressive (i.e. AR(1)) model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical ...

We consider the problem of constructing asymptotically valid confidence intervals for the change point in a high-dimensional covariance shift setting. A novel estimator for the change point parameter is developed, and its asymptotic distribution under ...

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic ...

The huge dependability on software systems has led to the need of reliable software in a short span of time. One of the ways to achieve this is to provide a series of versions of the software system. Thus, firms release the first software version with the ...

With the improvement of innovation, software developers consistently build up a new version of software by adding new features in the previously existing version of the software. In resent day's competitive market, the reliability and release time ...

A nonhomogeneous Poisson process‐based software reliability growth model (SGRM) has been developed incorporating change point, imperfect debugging, and fault dependency for multi‐release software. Parameters of the proposed SRGM are estimated using ...

A bent line quantile regression model can describe the conditional quantile function of the response variable with two different straight lines, which intersect at an unknown change point. This paper proposes a new approach via a smoothing ...

We consider the problem of estimating the location of a single change point in a network generated by a dynamic stochastic block model mechanism. This model produces community structure in the network that exhibits change at a single time epoch. We ...

This paper presents a unified approach to model the reliability growth of software with imperfect debugging and coverage factor. Existing testing coverage‐based software reliability growth models considered that faults present at a particular ...

Let Y = mX + ε be a regression model with a dichotomous output Y and a one-step regression function m. In the literature, estimators for the three parameters of m, that is, the breakpoint ï ź and the levels a and b, are proposed for independent and ...

An interesting scheme for estimating and adapting distributions in real-time for non-stationary data has recently been the focus of study for several different tasks relating to time series and data mining, namely change point detection, outlier ...

Change point detecting problem is an important task in data mining applications. Standard statistical procedures for change point detection, based on maximum likelihood estimators, are complex and require building of parametric models of data. Instead, ...

Despite the fact that control charts are able to trigger a signal when a process has changed, it does not indicate when the process change has begun. The time difference between the changing point and a signal of a control chart could cause confusions ...

Monitoring its health by detecting its failed sensors is essential to the reliable functioning of any sensor network. This paper presents a distributed, online, sequential algorithm for detecting multiple faults in a sensor network. The algorithm works ...

Online detection of outliers and change points from a data stream are two very exciting topics in the area of data mining. This paper explores the relationship between these two issues, and presents a unifying method for dealing with both of them. ...

Long memory processes are widely used in many scientific fields, such as economics, physics, and engineering. Change point detection problems have received considerable attention in the literature because of their wide range of possible applications. ...

We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in these issues since outlier detection is related to fraud detection, rare event discovery, etc., ...