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1
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Detection of outlying observations using the Akaike information criterion

100%
Kornacki A.
Biometrical Letters
|
2013
|
tom 50
|
nr 2
117-126
EN
For the detection of outliers (observations which are seemingly different from the others) the method of testing hypotheses is most often used. This approach, however, depends on the level of significance adopted by the investigator. Moreover, it can lead to the undesirable effect of “masking” of the outliers. This paper presents an alternative method of outlier detection based on the Akaike information criterion. The theory presented is applied to analysis of the results of beet leaf mass determination.
2
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A Bayesian model to compare vinification procedures

100%
Stefanini F. M., Pantani O.-L.
Biometrical Letters
|
2013
|
tom 50
|
nr 2
61-80
EN
The effects of three pre-fermentative techniques (standard procedure, cold soak pre-fermentation and cryomaceration), temperature (20 or 30°C) and saignée (with/without) on the extraction of total anthocyanins were investigated during maceration of must obtained from Sangiovese grapes. A Bayesian hierarchical model was developed to estimate time-dependent contrasts while addressing the peculiar features displayed by the experimental units (wine tanks): substantial heterogeneity among replicates, departure from low-order `textbook' kinetics and the occasional presence of very low observations. Prior distributions of critical model parameters were elicited with the help of wine{making experts and by considering the results of previous experiments. The posterior distribution of model parameters was approximated by Markov Chain Monte Carlo simulation using JAGS software. Among the main findings, it is to be highlighted that temperature and saignée increased the total anthocyanin concentration in all the techniques, although at different times during maceration. In all the procedures the total anthocyanin gain decreased as the maceration came to an end.
3
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Tail orderings and the total time on test transform

100%
Bartoszewicz J.
Applicationes Mathematicae
|
1996-1997
|
tom 24
|
nr 1
77-86
EN
The paper presents some connections between two tail orderings of distributions and the total time on test transform. The procedure for testing the pure-tail ordering is proposed.
4
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Fault detection and isolation with robust principal component analysis

86%
Tharrault Y., Mourot G., Ragot J., Maquin D.
International Journal of Applied Mathematics and Computer Science
|
2008
|
tom 18
|
nr 4
429-442
EN
Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a “local” matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.
5
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The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments

86%
Kornacki A., Bochniak A.
Biometrical Letters
|
2015
|
tom 52
|
nr 2
75-84
EN
In this study the Akaike information criterion for detecting outliers in a log-normal distribution is used. Theoretical results were applied to the identification of atypical varietal trials. This is an alternative to the tolerance interval method. Detection of outliers with the help of the Akaike information criterion represents an alternative to the method of testing hypotheses. This approach does not depend on the level of significance adopted by the investigator. It also does not lead to the masking effect of outliers.
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