Goodness-of-fit tests in the multi-state Markov model

Kong, Yaonan; Ye, Zhi‐Sheng

doi:10.1016/j.ress.2017.02.010

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…The goodness to fit test criteria are assessed through the root standard square residual (SRMR). The value is considered appropriate if <0.10 or 0.09 (Kong and Ye, 2017).…”

Section: Goodness To Fitmentioning

confidence: 99%

Religiosity and Tax Compliance: Does Tax Knowledge Is Matter?

2022

SAR

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Tujuan dari penelitian ini adalah menguji pengaruh religiusitas terhadap kepatuhan wajib pajak dan menguji peran moderasi pengetahuan pajak terhadap hubungan antara religiusitas dan kepatuhan pajak tersebut. Penelitian ini menggunakan pendekatan kuantitatif dengan purposive sampling sebagai teknik pengambilan sampel. Kuesioner dibagikan secara elektonik kepada 124 wajib pajak yang berdomisili di Karawang. Hasil pengujian statistik dengan menggunakan structural equation modelling (SEM) menunjukkan bahwa religiusitas mempunyai pengaruh positif signifikan terhadap kepatuhan pajak. Selain itu, pengetahuan perpajakan juga memiliki pengaruh positif signifikan, sehingga dapat diartikan bahwa pengetahuan perpajakan tersebut dapat memperkuat hubungan antara religiusitas dengan kepatuhan pajak. Oleh karena itu penting untuk fiscus mendorong dan meningkatkan adanya sosialisasi perpajakan agar wajib pajak memahami prosedur dan ketentuan-ketentuan perpajakan. Pada akhirnya, kepatuhan perpajakan dapat ditingkatkan.

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“…The goodness to fit test criteria are assessed through the root standard square residual (SRMR). The value is considered appropriate if <0.10 or 0.09 (Kong and Ye, 2017).…”

Section: Goodness To Fitmentioning

confidence: 99%

Religiosity and Tax Compliance: Does Tax Knowledge Is Matter?

2022

SAR

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“…Statistical analysis, such as regression analysis and variance analysis, also requires normality testing of data to be performed in advance. The test methods include the Kolmogorov-Smirnov (K-S), Shapiro-Wilk, and Ryan-Joiner methods, and some goodness of fit tests, such as the chi-square, Cramer-von Mises, and Anderson-Darling (A-D) methods [42].…”

Section: Normality Test and Power Transformationmentioning

confidence: 99%

A Quantitative Validation Method of Kriging Metamodel for Injection Mechanism Based on Bayesian Statistical Inference

You

Shen

Zhu

et al. 2019

Metals

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A Bayesian framework-based approach is proposed for the quantitative validation and calibration of the kriging metamodel established by simulation and experimental training samples of the injection mechanism in squeeze casting. The temperature data uncertainty and non-normal distribution are considered in the approach. The normality of the sample data is tested by the Anderson–Darling method. The test results show that the original difference data require transformation for Bayesian testing due to the non-normal distribution. The Box–Cox method is employed for the non-normal transformation. The hypothesis test results of the calibrated kriging model are more reliable after data transformation. The reliability of the kriging metamodel is quantitatively assessed by the calculated Bayes factor and confidence. The Bayesian factor and the confidence level results indicate that the kriging model demonstrates improved accuracy and is acceptable after data transformation. The influence of the threshold ε on both the non-normally and normally distributed data in the model is quantitatively evaluated. The threshold ε has a greater influence and higher sensitivity when applied to the normal data results, based on the rapid increase within a small range of the Bayes factors and confidence levels.

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“…where k = i + 1 if i \ K or k = 1 if i = K. Substituting equations (10) and (12) into equation 9, we have…”

Section: ð3þmentioning

confidence: 99%

“…Some of the recent research efforts that involve the use of gamma processes include Caballe´et al, 8 Ling et al, 9 and Deloux et al 10 In most existing researches, degradation behavior is modeled by a homogeneous stochastic process. 11,12 For gamma process-based degradation modeling, this means that degradation increment in each unit time follows an identical distribution. The homogeneous assumption is reasonable especially when the working environment of the system is stable.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling for systems degrading in K cyclical regimes based on gamma processes

Shen

Elwany

Cui

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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Regime switching models represent an important research direction in reliability engineering and degradation modeling. In practice, complex engineering systems typically operate under variable environmental conditions or mission requirements, each having distinct influence of system degradation. In this article, we study a system deteriorating in K cyclical regimes, where the degradation behavior of the system during each regime is governed by a distinct gamma process. We first derive the cumulative distribution function of the first passage time under this setting. Next, we determine system availability when maintenance actions are taken into consideration under the scenario where regime switching only takes place during the system uptime. Finally, we give some numerical examples to demonstrate and validate the proposed framework.

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Goodness-of-fit tests in the multi-state Markov model

Cited by 16 publications

References 29 publications

Religiosity and Tax Compliance: Does Tax Knowledge Is Matter?

Religiosity and Tax Compliance: Does Tax Knowledge Is Matter?

A Quantitative Validation Method of Kriging Metamodel for Injection Mechanism Based on Bayesian Statistical Inference

Reliability modeling for systems degrading in K cyclical regimes based on gamma processes

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