Efficient algorithms for robust estimation in autoregressive regression models using Student’s<i>t</i>distribution

Nduka, Uchenna Chinedu; Iwueze, Iheanyi S.; Nwogu, Elezar C.

doi:10.1080/03610918.2018.1484481

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…In 1908, W.S. Gosset proposed the t distribution which regards the degree of freedom n as a variable [23]. The probability density function is given by:…”

Section: Adaptive T Distribution Mutationmentioning

confidence: 99%

Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Hua

Xiao

Cao

2021

Entropy

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A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and theprinciple of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.

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“…In 1908, W.S. Gosset proposed the t distribution which regards the degree of freedom n as a variable [23]. The probability density function is given by:…”

Section: Adaptive T Distribution Mutationmentioning

confidence: 99%

Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Hua

Xiao

Cao

2021

Entropy

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“…Alkhatib et al 2018). Algorithms for the simpler case of linear, univariate time series with AR and t-distributed random errors were independently investigated by Kargoll et al (2018a), Tuac et al (2018 and Nduka et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors

Kargoll

Kermarrec

Korte

et al. 2020

J Geod

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The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. 10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN).

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Modeling serially correlated heavy-tailed data with some missing response values using stochastic EM algorithm

Nduka

Iwueze

Nwaigwe

2021

Communications in Statistics: Case Studies, Data Analysis and A

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Efficient algorithms for robust estimation in autoregressive regression models using Student’stdistribution

Cited by 4 publications

References 25 publications

Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors

Modeling serially correlated heavy-tailed data with some missing response values using stochastic EM algorithm

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