Introduction to Stochastic Processes With R

Dobrow, Robert P.

doi:10.1002/9781118740712

Cited by 49 publications

(54 citation statements)

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“…Each of the sample points x1,…,xd of the sample is allowed to take a discrete or continuous time value which denotes the project ages already known from the given repository (D). The Markov chain is then executed a number of times until the chain converges to its limiting distribution to obtain the target sample subset [6]. The selected sample with its respective t states (or ages) and constructed limiting distribution forms the Bellwether which can be used as the moving window.…”

Section: B Markov Chain Monte Carlomentioning

confidence: 99%

“…Assume there exist a variable 'P' that can be formulated as a matrix of transition probabilities of a Markov chain [6] from the sample space {X (1) ,…,X (t) } where i T denotes the transition states (project ages), then the ij th element, p (k) ij P is the probability that the Markov chain starting from a particular state, ti will transition to tj after k steps. If p (k) ij is homogeneous, then (2) holds and there exist a unique probability matrix, u that can form the ergodic Markov chain (EMC) such that for any o and for large values of u, (3) can be defined as follows:…”

Section: ) If P Denotes a Regular Transition Probability Matrix (Tpmmentioning

confidence: 99%

“…The Markov chain Monte Carlo (MCMC) is a probabilistic technique that seeks to solve the problem of sampling by exploring a given sample space through the construction of an ergodic Markov chain (EMC) whose limiting distribution is the target distribution [6]. MCMC utilizes Markov chains to effectively simulate a random variable X whose future states are independent of past states given the present state.…”

Section: B Markov Chain Monte Carlomentioning

confidence: 99%

“…The concept of Bellwether (specifically the Bellwether effect) and the Markov chains theory [6] are empirically proven to determine the best window age and window size of moving windows in this study.…”

Section: Introductionmentioning

confidence: 99%

“…The model operates by applying the concept of Bellwether [1] and a probabilistic modeling approach namely Markov chain Monte Carlo [6] to determine an effective size and age of the moving window used in modeling.…”

Section: Introductionmentioning

confidence: 99%

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A Stratification and Sampling Model for Bellwether Moving Window

Mensah

Keung

Bosu³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-An effective method for finding the relevant number (window size) and the elapsed time (window age) of recently completed projects has proven elusive in software effort estimation. Although these two parameters significantly affect the prediction accuracy, there is no effective method to stratify and sample chronological projects to improve prediction performance of software effort estimation models. Exemplary projects (Bellwether) representing the training set have been empirically validated to improve the prediction accuracy in the domain of software defect prediction. However, the concept of Bellwether and its effect have not been empirically proven in software effort estimation as a method of selecting exemplary/relevant projects with defined window size and age. In view of this, we introduce a novel method for selecting relevant and recently completed projects referred to as Bellwether moving window for improving the software effort prediction accuracy. We first sort and cluster a pool of N projects and apply statistical stratification based on Markov chain modeling to select the Bellwether moving window. We evaluate the proposed approach using the baseline Automatically Transformed Linear Model on the ISBSG dataset. Results show that (1) Bellwether effect exist in software effort estimation dataset, (2) the Bellwether moving window with a window size of 82 to 84 projects and window age of 1.5 to 2 years resulted in an improved prediction accuracy than the traditional approach.

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Section: B Markov Chain Monte Carlomentioning

confidence: 99%

Section: ) If P Denotes a Regular Transition Probability Matrix (Tpmmentioning

confidence: 99%

Section: B Markov Chain Monte Carlomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Stratification and Sampling Model for Bellwether Moving Window

Mensah

Keung

Bosu³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.

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Phylogenetics and Systematics in a Nutshell

Espinosa de los Monteros

2020

Avian Malaria and Related Parasites in the Tropics

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Introduction to Stochastic Processes With R

Cited by 49 publications

References 0 publications

A Stratification and Sampling Model for Bellwether Moving Window

A Stratification and Sampling Model for Bellwether Moving Window

A classification scheme to improve conclusion instability using Bellwether moving windows

Phylogenetics and Systematics in a Nutshell

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