On the contaminated exponential distribution: A theoretical Bayesian approach for modeling positive-valued insurance claim data with outliers

Okhli, Kheirolah; Nooghabi, Mehdi Jabbari

doi:10.1016/j.amc.2020.125712

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…The monitoring data of rolling bearings cannot avoid outliers and false fluctuations due to the interference of noise, working conditions, and environmental changes. These may greatly impact the performance of the model [ 25 ]. Using auto-associative kernel regression to reconstruct the signals can reduce the influence of outliers, so as to improve the robustness and recognition accuracy of the model [ 26 , 27 ].…”

Section: Multi-strategy Optimized Support Vector Data Descriptionmentioning

confidence: 99%

Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD

Feng

Wang

Liu

et al. 2023

Sensors

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In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings’ performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing’s degradation. The stability and accuracy of the model were effectively improved.

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Section: Multi-strategy Optimized Support Vector Data Descriptionmentioning

confidence: 99%

Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD

Feng

Wang

Liu

et al. 2023

Sensors

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“…There are several instances in the literature where the support of the pdf of some notable continuous distributions excludes the endpoint(s) even when the distribution clearly have densit(y/ies) at such point(s), few examples includes: the exponential distribution with pdf defined as

0

in both [34] and [35] omits 0 at the support of y even when

0

; the Marshall–Olkin generalized exponential distribution with pdf defined as

001

in [36] omits 0 at the support of y even when

0

; the unit Lindley distribution with pdf defined as

11001

in [37] omits 0 at the support of y even when it is clear that

01

; the unit-improved second-degree Lindley distribution with pdf defined as

221001

in [38] omits 0 at the support of y even when it is obvious that

022

; and the unit-Gompertz distribution with pdf defined as …”

Section: And 1 Inflated Unit Upper Truncated Weibull Distributionmentioning

confidence: 99%

Unit upper truncated Weibull distribution with extension to 0 and 1 inflated model – Theory and applications

Okorie,

Afuecheta,

Bakouch

2023

Heliyon

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“…When we ignore some cases in the data as the outliers, it leads losing information. For more details, refer to Jabbari Nooghabi (2019), Okhli and Jabbari Nooghabi (2021), Jabbari Nooghabi and Khaleghpanah Nooghabi (2016), and Dixit andJabbari Nooghabi (2011a, 2011b). Many researchers tried to estimate in the presence of outliers.…”

Section: Outliersmentioning

confidence: 99%

Comparing estimation of the parameters of distribution of the root density of plants in the presence of outliers

Nooghabi

2021

Environmetrics

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The root density of plants with depth follows exponential or the Lindley distribution in the presence of outliers generated from a uniform distribution. In this article, we estimate the parameters of the Lindley distribution in the presence of outliers generated from a uniform distribution based on the moment, maximum likelihood, least squares, weighted least squares, percentile, Cramer-von-Mises, and Anderson-Darling methods and mixture estimator of moment and maximum likelihood. These methods of estimation are compared. Also, the estimators of the parameters of Lindley-uniform contaminated distribution are compared with the corresponding estimators of exponential-uniform contaminated distribution, which was presented by Dixit and Nasiri, Metron, 59(3-4), 187-198 (2001). Furthermore, an analysis of an actual example of the root length of plants is presented for illustrative purposes. It is concluded that the Lindley-uniform contaminated distribution is more appropriate than the exponential-uniform contaminated distribution to model the root density of plants.

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On the contaminated exponential distribution: A theoretical Bayesian approach for modeling positive-valued insurance claim data with outliers

Cited by 8 publications

References 34 publications

Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD

Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD

Unit upper truncated Weibull distribution with extension to 0 and 1 inflated model – Theory and applications

Comparing estimation of the parameters of distribution of the root density of plants in the presence of outliers

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