Application of the Akaike Criterion to Detect Outliers for the Analysis of ash Content in Barley Straw

Kornacki, Andrzej

doi:10.2478/intag-2014-0015

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Ueda (2009) presented a simple and efficient method to detect multiple outliers using a modification of the AIC, and it has been successfully applied to sample observations. Kornacki (2014) applied the AIC to the detection of outliers for the analysis of ash content in barley straw. The author also comes to the conclusion that the method has two advantages: (1) it "is an objective procedure independent of the assumed significance level, quantity of outliers and of whether the suspicious observations are the lowest or the highest" and (2) it avoids the masking effect of outlier detection.…”

Section: Introductionmentioning

confidence: 99%

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

Lehmann

Lösler

2016

J. Surv. Eng.

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The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.

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Section: Introductionmentioning

confidence: 99%

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

Lehmann

Lösler

2016

J. Surv. Eng.

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Glyphosate spraying exacerbates nitrogen and phosphorus loss in karst slope farmland

Zhang,

Yan,

Dai

et al. 2023

Environ Monit Assess

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Influences of tobacco straw return with lime on microbial community structure of tobacco-planting soil and tobacco leaf quality

jiang,

zu,

Riaz

et al. 2023

Preprint

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Soil amendment is an important strategy to improve soil quality and crop yield. During 2014–2019, we investigated the effects of tobacco straw return with lime on soil nutrients, soil microbial community structure, tobacco leaf yield and quality in southern Anhui, China. A field experiment was conducted in four treatments: straw removed (CK), straw return (St), straw return with dolomite (St + D), and straw return with lime (St + L). Results showed that after five years of continuous treatment, the St + L significantly increased the soil pH by 16.9%, and the contents of soil alkaline nitrogen (N) and available potassium (K) by 17.2% and 23.0%, respectively, compared with the CK. Moreover, the St + L significantly increased tobacco leaf yield (24.0%) and the appearance (9.1%) and sensory (5.9%) quality of flue-cured tobacco leaves. The addition of soil conditioners (straw, dolomite, and lime) resulted in an increase in both the total reads and effective sequences of soil microorganisms. Bacterial diversity was found to be more sensitive to changes in the external environment compared to soil fungi. The application of soil amendments (lime and straw) has been shown to promote the growth of beneficial microorganisms in the soil. Additionally, bacterial species face greater competition and limited availability of resources for survival compared to fungi. Various analytical techniques, such as RDA, correlation analysis, and network analysis, were employed to investigate this finding: soil microorganisms are significantly influenced by the presence of AK, AN and pH contents. These findings can provide an effective method for improving the quality of flue-cured tobacco leaves and guiding the amelioration of acidic soil in regions where tobacco-rice rotation is practiced.

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Application of the Akaike Criterion to Detect Outliers for the Analysis of ash Content in Barley Straw

Abstract: This study presents the method of detection of outliers based on the Akaike information criterion. This method has been applied to experimental data on ash content resulting from the combustion of barley straw.

Cited by 4 publications

References 17 publications

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria

Glyphosate spraying exacerbates nitrogen and phosphorus loss in karst slope farmland

Influences of tobacco straw return with lime on microbial community structure of tobacco-planting soil and tobacco leaf quality

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