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

2014 ◽  
Vol 28 (2) ◽  
pp. 257-260 ◽  
Author(s):  
Andrzej Kornacki
Keyword(s):  
Experimental Data ◽  
Akaike Information Criterion ◽  
Information Criterion ◽  
Ash Content ◽  
Barley Straw ◽  
Akaike Criterion ◽  
Detection Of Outliers

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.

Optimal Order of Markov Models Applied to Bankchecks

1997 ◽  
Vol 11 (05) ◽  
pp. 789-800 ◽  
Author(s):  
C. Olivier ◽  
T. Paquet ◽  
M. Avila ◽  
Y. Lecourtier
Keyword(s):  
Markov Model ◽  
Structural Properties ◽  
Conditional Probability ◽  
Akaike Information Criterion ◽  
Markov Models ◽  
Information Criterion ◽  
Optimal Order ◽  
Structural Representation ◽  
Akaike Criterion

The aim of this study is to show that the optimal order of Markov Model of cursive words can be rigorously stated in order to fit the structural properties of the observed data using Akaike information criterion. The method has been tested on French Postal check amounts up to order 4. An original structural representation of cursive words based on graphemes is used. The conditional probability to have a word model given an observed sequence of graphemes is computed independently of the length of the sequence. The recognition results obtained confirm the optimal order found using Akaike criterion.

scholarly journals Dynamic Model for Biomass and Proteins Production by Three Bacillus Thuringiensis ssp Kurstaki Strains

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2147
Author(s):  
Tatiana Segura Monroy ◽  
Nouha Abdelmalek ◽  
Souad Rouis ◽  
Mireille Kallassy ◽  
Jihane Saad ◽  
...  
Keyword(s):  
Experimental Data ◽  
Bacillus Thuringiensis ◽  
Dynamic Model ◽  
Akaike Information Criterion ◽  
Total Protein ◽  
Kinetic Models ◽  
Information Criterion ◽  
Growth Limitation ◽  
Protein Productivity ◽  
Different Strains

Bacillus thuringiensis is a microorganism used for the production of biopesticides worldwide. In the present paper, different kinetic models were analyzed to study and compare three different strains of Bt ssp kurstaki (LIP, BLB1, and HD1). Bioperformances (vegetative cell, spore, substrate, and protein) and successive culture phases (oxidative growth, limitation and sporulation, and protein release) were depicted with an overarching aim to estimate total protein productivity, yield, and titer. In the end, two models were calibrated using experimental dataset (11 batches culture in 3 L bioreactor with semisynthetic medium), subsequently validated, and statistically compared. Both models satisfactorily followed the dynamics of the experimental data. Finally, a dynamic model was selected following the Akaike information criterion (AIC).

scholarly journals Detection of outlying observations using the Akaike information criterion

2013 ◽  
Vol 50 (2) ◽  
pp. 117-126
Author(s):  
Andrzej Kornacki
Keyword(s):  
Outlier Detection ◽  
Akaike Information Criterion ◽  
Information Criterion ◽  
Mass Determination ◽  
Undesirable Effect ◽  
Testing Hypotheses ◽  
Leaf Mass ◽  
Alternative Method ◽  
Level Of Significance ◽  
Detection Of Outliers

Summary 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.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Complexity versus simplicity: an example of groundwater model ranking with the Akaike Information Criterion

2012 ◽  
Vol 9 (8) ◽  
pp. 9687-9714 ◽  
Author(s):  
I. Engelhardt ◽  
J. G. De Aguinaga ◽  
H. Mikat ◽  
C. Schüth ◽  
O. Lenz ◽  
...  
Keyword(s):  
Akaike Information Criterion ◽  
Conceptual Models ◽  
Information Criterion ◽  
Model Parameters ◽  
Groundwater Model ◽  
Piezometric Head ◽  
Simulated Groundwater ◽  
Pumping Rates ◽  
Uncalibrated Model ◽  
Piezometric Heads

Abstract. A groundwater model characterized by a lack of field data to estimate hydraulic model parameters and boundary conditions combined with many piezometric head observations was investigated concerning model uncertainty. Different conceptual models with a stepwise increase from 0 to 30 adjustable parameters were calibrated using PEST. Residuals, sensitivities, the Akaike Information Criterion (AIC), and the likelihood of each model were computed. As expected, residuals and standard errors decreased with an increasing amount of adjustable model parameters. However, the model with only 15 adjusted parameters was evaluated by AIC as the best option with a likelihood of 98%, while the uncalibrated model obtained the worst AIC value. Computing of the AIC yielded the most important information to assess the model likelihood. Comparing only residuals of different conceptual models was less valuable and would result in an overparameterization of the conceptual model approach. Sensitivities of piezometric heads were highest for the model with five adjustable parameters reflecting also changes of extracted groundwater volumes. With increasing amount of adjustable parameters piezometric heads became less sensitive for the model calibration and changes of pumping rates were no longer displayed by the sensitivity coefficients. Therefore, when too many model parameters were adjusted, these parameters lost their impact on the model results. Additionally, using only sedimentological data to derive hydraulic parameters resulted in a large bias between measured and simulated groundwater level.

scholarly journals Drying Kinetics of Noni Seeds

2019 ◽  
Vol 11 (5) ◽  
pp. 250 ◽  
Author(s):  
Wellytton Darci Quequeto ◽  
Osvaldo Resende ◽  
Patrícia Cardoso Silva ◽  
Fábio Adriano Santos e Silva ◽  
Lígia Campos de Moura Silva
Keyword(s):  
Experimental Data ◽  
Activation Energy ◽  
Diffusion Coefficient ◽  
Moisture Content ◽  
Mathematical Models ◽  
Effective Diffusion Coefficient ◽  
Effective Diffusion ◽  
Information Criterion ◽  
Drying Kinetics ◽  
Kinetics Of

Noni seeds have been used for years as an important medicinal source, with wide use in the pharmaceutical and food industry. Drying is a fundamental process in the post-harvest stages, where it enables the safe storage of the product. Therefore, the present study aimed to fit different mathematical models to experimental data of drying kinetics of noni seeds, determine the effective diffusion coefficient and obtain the activation energy for the process during drying under different conditions of air temperature. The experiment used noni seeds with initial moisture content of 0.46 (decimal, d.b.) and dehydrated up to equilibrium moisture content. Drying was conducted under different controlled conditions of temperature, 40; 50; 60; 70 and 80 ºC and relative humidity, 24.4; 16.0; 9.9; 5.7 and 3.3%, respectively. Eleven mathematical models were fitted to the experimental data. The parameters to evaluate the fitting of the mathematical models were mean relative error (P), mean estimated error (SE), coefficient of determination (R2), Chi-square test (c2), Akaike Information Criterion (AIC) and Schwarz’s Bayesian Information Criterion (BIC). Considering the fitting criteria, the model Two Terms was selected to describe the drying kinetics of noni seeds. Effective diffusion coefficient ranged from 8.70 to 23.71 × 10-10 m2 s-1 and its relationship with drying temperature can be described by the Arrhenius equation. The activation energy for noni seeds drying was 24.20 kJ mol-1 for the studied temperature range.

scholarly journals Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"

2018 ◽  
Vol 10 (1) ◽  
pp. 80-87
Author(s):  
Surobhi Deka
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Akaike Information Criterion ◽  
Markov Chain Model ◽  
Information Criterion ◽  
Chain Model ◽  
Adequate Model ◽  
First Order ◽  
North East India ◽  
North East

The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain model of appropriate order for analyzing wet and dry days is determined. This is done using the Akaike Information Criterion (AIC) by checking the minimum of AIC estimate. Markov chain of order one is found to be superior to the majority of the stations in comparison to the other order Markov chains. More precisely, first order Markov chain model is an adequate model for the stations North Bank, Tocklai, Silcoorie, Mohanbari and Guwahati. Further, it is observed that second order and third order Markov chains are competing with first order in the stations Cherrapunji and Imphal, respectively. A fore-knowledge of rainfall pattern is of immense help not only to farmers, but also to the authorities concerned with planning of irrigation schemes. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

scholarly journals Autoregressive Modeling and Forecasts of Degema Monthly Allocation: Buy’s-Ballot and Bartlett’s Transformation

2021 ◽  
pp. 61-75
Author(s):  
Herbert, AfeyaIbibo ◽  
Biu, Oyinebifun Emmanuel ◽  
Enegesele, Dennis ◽  
Wokoma, Dagogo Samuel Allen
Keyword(s):  
Autocorrelation Function ◽  
Akaike Information Criterion ◽  
Selection Criteria ◽  
Linear Trend ◽  
Information Criterion ◽  
Transformation Method ◽  
Autoregressive Modeling ◽  
Data Set ◽  
Trend Pattern ◽  
Partial Autocorrelation Function

The paper focused on Autoregressive modeling and forecasts of Degema Local Government Council Monthly Allocation (DLGCMA) in River State, Nigeria. The Buys-Ballot table and Bartlett’s Transformation method were adopted to identify the trend pattern and to determine the best transformation for the series. The logarithmic transformation was adjudged to be the best and was applied to stabilize the variance. Identification of the trend and stationary for the data set was done and the DLGCMA series showed a linear trend that was non-stationary. The stationarity of the DLGCMA series was obtained after the first difference. The ARIMA models were fitted to the series base on the behaviour of autocorrelation function (ACF) and partial autocorrelation function (PACF). Finally, the model selection criteria called Akaike information criterion was used to determine the best model among the predicted models. The AR(3,1,0) model ( Xt = 0.56Xt-1 + 0.17Xt-2 + 0.64Xt-3 - 0.37Xt-4 + et) was considered to be the best model because it has the least value of the Akaike information criterion (AIC). Hence, the forecasts for the next allocation of twenty-four (24) months ahead were determined.

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