The paper discusses the effectiveness of the method for determining the parameters of the useful signal in the sliding window mode, provided that it is possible to obtain preliminary estimates for the noise distributi...
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The paper discusses the effectiveness of the method for determining the parameters of the useful signal in the sliding window mode, provided that it is possible to obtain preliminary estimates for the noise distribution. For the statistical experiment, samples had been generated with different ratios between the signal and noise parameters. Implementation of the computational procedures for the adaptive method in the Python programming language is proposed. For the test samples, it is demonstrated that the magnitude of the error in evaluating the parameters in the vast majority of cases does not exceed value 1 (in terms of the standard RMSE metrics). In addition, an effective two-pass method for detecting the moment of the appearance of a meaningful signal in the noisy data is proposed. The results of its operation are also demonstrated on the example of the mentioned test samples.
In order to effectively conduct the defect detection of nuclear fuel pellets end face and avoid the leakage of nuclear radiation, a defect detection system for the nuclear fuel pellets end face based on machine vision...
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In order to effectively conduct the defect detection of nuclear fuel pellets end face and avoid the leakage of nuclear radiation, a defect detection system for the nuclear fuel pellets end face based on machine vision is proposed. Firstly, aiming at the complexity of the defect detection of nuclear fuel pellets, a set of image acquisition system lighted by left-right symmetric grating is designed. Then, after fusing the images of left-right structured light those cross points are extracted which classified based on the Gaussian mixture model (GMM). Finally, a series of morphological operations such as dilation operation are conducted for the classified points to obtain the defect area of nuclear fuel pellets end face. The experimental results show that this method reduces the influence of complex characteristics of form, texture, and color of the sample end face on the defect detection and relatively good detection results are gained for various defects with 99.5% accuracy. It takes less than 0.4 s to fully meet the requirements of industrial automation testing.
Repair delays are common in systems where failures are not self-revealed or in mission-critical systems where repairs cannot be conducted during a mission. The presence of repair delays may significantly affect the fa...
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Repair delays are common in systems where failures are not self-revealed or in mission-critical systems where repairs cannot be conducted during a mission. The presence of repair delays may significantly affect the failure behavior of a system, but its impact has been largely overlooked in the literature. In this article, we propose a flexible intensity-based model for repairable systems subject to repair delays. Statistical inference of the model is discussed. In the presence of repair delays, the exact failure times are typically unavailable and the failures are only known to occur at times within intervals with known lower and upper bounds. In this case, the likelihood function is complicated. We introduce several efficient numerical integration methods to evaluate the likelihood function and investigate their performance through comprehensive simulations. Goodness-of-fit tests are used to check the adequacy of the baseline rate of occurrence of failures and the effect of repair delays. The proposed methods are demonstrated using maintenance data of a general infusion pump used in a hospital.
This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) di...
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This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) distribution, which allows to model the quantiles of all univariate conditional distributions of a multivariate response simultaneously, incorporating the correlation structure among the outcomes. Unobserved serial heterogeneity across observations is modeled by introducing regime-dependent parameters that evolve according to a latent finite-state semi-Markov chain. Exploiting the hierarchical representation of the MAL, inference is carried out using an efficient Expectation-Maximization algorithm based on closed form updates for all model parameters, without parametric assumptions about the states' sojourn distributions. The validity of the proposed methodology is analyzed both by a simulation study and through the empirical analysis of air pollutant concentrations in a small Italian city.
In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity....
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In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity. Heterogeneous mixture machine learning is one algorithm for analyzing such data efficiently. In this study, we propose online heterogeneous learning based on an online em algorithm. Experiments show that this algorithm has higher learning accuracy than that of a conventional method and is practical. The online learning approach will make this algorithm useful in the field of data analysis.
Finite mixtures present a powerful tool for modeling complex heterogeneous data. One of their most important applications is model-based clustering. It assumes that each data group can be reasonably described by one m...
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Finite mixtures present a powerful tool for modeling complex heterogeneous data. One of their most important applications is model-based clustering. It assumes that each data group can be reasonably described by one mixture model component. This establishes a one-to-one relationship between mixture components and clusters. In some cases, however, this relationship can be broken due to the presence of observations from the same class recorded in different ways. This effect can occur because of recording inconsistencies due to the use of different scales, operator errors, or simply various recording styles. The idea presented in this paper aims to alleviate this issue through modifications incorporated into mixture models. While the proposed methodology is applicable to a broad class of mixture models, in this paper it is illustrated on Gaussian mixtures. Several simulation studies and an application to a real-life data set are considered, yielding promising results.
We consider the inpainting problem for noisy images. It is very challenge to suppress noise when image inpainting is processed. An image patches based nonlocal variational method is proposed to simultaneously inpainti...
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We consider the inpainting problem for noisy images. It is very challenge to suppress noise when image inpainting is processed. An image patches based nonlocal variational method is proposed to simultaneously inpainting and denoising in this paper. Our approach is developed on an assumption that the small image patches should be obeyed a distribution which can be described by a high dimension Gaussian Mixture Model. By a maximum a posteriori (MAP) estimation, we formulate a new regularization term according to the log-likelihood function of the mixture model. To optimize this regularization term efficiently, we adopt the idea of the Expectation Maximization (em) algorithm. In which, the expectation step can give an adaptive weighting function which can be regarded as a nonlocal connections among pixels. Using this fact, we built a framework for non-local image inpainting under noise. Moreover, we mathematically prove the existence of minimizer for the proposed inpainting model. By using a splitting algorithm, the proposed model are able to realize image inpainting and denoising simultaneously. Numerical results show that the proposed method can produce impressive reconstructed results when the inpainting region is rather large. (C) 2020 Published by Elsevier Inc.
In this article, we conduct objective Bayesian analysis for the mixture cure model based on the Weibull distribution with right-censored data. By introducing latent variables, the complete likelihood function of the m...
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In this article, we conduct objective Bayesian analysis for the mixture cure model based on the Weibull distribution with right-censored data. By introducing latent variables, the complete likelihood function of the model is given and from that the Fisher information matrix is obtained by approximation. We obtain the maximum likelihood estimates by em algorithm, and derive objective priors including Jeffreys prior, reference priors, and matching probability priors to carry out Bayesian estimation. A simulation study and a real data analysis illustrate the methods proposed in this article, and show that the objective Bayesian method gives better performance under small sample sizes compared to maximum likelihood method.
This study examines the relationship between interest rate and defunct platform risk of China's peer-to-peer (P2P) lending platforms. P2P lending provides an alternative funding source for individuals and micro-en...
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This study examines the relationship between interest rate and defunct platform risk of China's peer-to-peer (P2P) lending platforms. P2P lending provides an alternative funding source for individuals and micro-enterprises and offers a new investment tool for households. But the frequent collapses of many platforms were huge losses to market participants and even led to a decline in China's P2P lending industry. In this study, weekly data of 76 platforms from December 3, 2017, to October 6, 2019, are employed, and empirical research based on the normal and skew-normal panel data model respectively is conducted. Statistical indicators prove that the skew-normal panel data model is preferable to another one in modeling the data set of interest rates. The empirical results show that China's P2P market is efficient overall. But the positive correlation between the interest rate and risk is not significant for platforms with excessively high interest rates, whose interest rates are more determined by the types of ownership. The findings and implications are provided in the end.
This paper aims to unfold the information content of the implied liquidity measure, which is introduced through the Conic Finance theory and considered a proxy for the market liquidity level. We propose a partial info...
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This paper aims to unfold the information content of the implied liquidity measure, which is introduced through the Conic Finance theory and considered a proxy for the market liquidity level. We propose a partial information setting in which the dynamics of the implied liquidity, representing the noisy information on the unobserved true market liquidity, follow a continuous-time Markov-chain modulated exponential Ornstein-Uhlenbeck process. Model inference requires the filtering of the unobserved states of the true market liquidity, as well as the estimation of the unknown model parameters. We address the inference problem using the em algorithm methodology, in which we provide novel results on robust filters leading to maximum likelihood estimates. We fit the proposed model to the implied liquidity series obtained from the prices of (closest to) 1-year ATM call options on the S & P 500 covering the period from January 2002 to August 2022. The data application shows that the unobserved true market liquidity follows three regimes. The implied liquidity series contains relevant information as the filtered trajectory of the underlying Markov chain moves according to the economic environment changes due to the Federal Reserve's actions, the global financial crisis of 2007-08, and the COVID-19 pandemic.
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