Akaike information criterion applied to detecting first arrival times on microseismic data

2011 ◽  
Author(s):  
Andy St‐Onge
Keyword(s):  
Akaike Information Criterion ◽  
Information Criterion ◽  
Arrival Times ◽  
Microseismic Data ◽  
First Arrival

First arrival time picking for microseismic data based on DWSW algorithm

2018 ◽  
Vol 22 (4) ◽  
pp. 833-840 ◽  
Author(s):  
Yue Li ◽  
Yue Wang ◽  
Hongbo Lin ◽  
Tie Zhong
Keyword(s):  
Arrival Time ◽  
Microseismic Data ◽  
First Arrival ◽  
First Arrival Time ◽  
Arrival Time Picking

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 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.

scholarly journals COMPARISON OF MATHEMATICAL MODELS FOR THE USE OF ENROFLOXACIN IN DOGS

2019 ◽  
Vol 7 (2) ◽  
pp. 97-102 ◽  
Author(s):  
Miroslav Vasilev ◽  
Galya Shivacheva
Keyword(s):  
Differential Equation ◽  
Mathematical Models ◽  
Blood Plasma ◽  
Akaike Information Criterion ◽  
Order Differential Equation ◽  
Information Criterion ◽  
Second Order ◽  
Differential Model ◽  
Single Intravenous Injection ◽  
Future Developments

This article analyzes the process of changing the concentration of enrofloxacin in blood plasma in dogs after a single intravenous injection of the substance. Three mathematical models are proposed - algebraic and two models, based on a differential equation of first and second order. Identification of their parameters has been performed. Based on Akaike information criterion corrected as the best model was chosen the represented by a second-order differential equation. Three equations are identified and the exact numerical values of their parameters are obtained. For the evaluation and comparison of the three models, Akaike information criterion was used. The best results showed the second-order differential model. It will be used in future developments.

scholarly journals Sensor-based localization of epidemic sources on human mobility networks

2021 ◽  
Vol 17 (1) ◽  
pp. e1008545
Author(s):  
Jun Li ◽  
Juliane Manitz ◽  
Enrico Bertuzzo ◽  
Eric D. Kolaczyk
Keyword(s):  
Mixture Model ◽  
Human Mobility ◽  
Gaussian Mixture ◽  
Local Conditions ◽  
Arrival Times ◽  
Modes Of Transmission ◽  
Kwazulu Natal ◽  
First Arrival ◽  
Control Of Epidemics ◽  
The Impact

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.

scholarly journals Identificación de un modelo lineal adecuado para evaluar características pre-destete en ganado criollo colombiano Blanco Orejinegro

2021 ◽  
Vol 26 (1) ◽  
pp. 49-56
Author(s):  
Luisa Fernanda Naranjo Guerrero ◽  
Alberiro López Herrera ◽  
Juan Carlos Rincon Florez ◽  
Luis Gabriel González Herrera
Keyword(s):  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Modelo Lineal

La Raza criolla Blanco Orejinegro (BON) tiene un proceso de adaptación de más de 500 años a las condiciones ambientales de Colombia. Se caracteriza por ser una raza doble propósito utilizada para la producción de leche y carne, convirtiéndola en un patrimonio biológico de gran importancia que debe ser estudiado. El objetivo de este estudio fue identificar un modelo lineal adecuado para evaluar características pre-destete en ganado criollo Blanco Orejinegro. Se recolectó y depuró información de pesajes de cuatro hatos de ganado BON. Las características evaluadas fueron peso a los 4 meses (P4M), peso al destete (PD) y ganancia diaria de peso entre los 4 meses y el destete (GDP4M-D). Se evaluaron nueve modelos lineales en los que se incluyeron como efectos fijos los siguientes factores: sexo, hato, mes de pesaje o nacimiento, número de parto, época de pesaje o época de nacimiento (época seca o lluviosa), edad (covariable, efecto fijo y ajustada por regresión), año de pesaje o año de nacimiento y grupo contemporáneo (GC) compuesto por sexo y hato para GDP4M-D y sexo, hato y año de pesaje para P4M y PD, con mínimo cinco observaciones por GC. Para identificar el modelo lineal más adecuado para cada característica se utilizó el valor de AIC (Akaike information criterion), BIC (Bayesian information criterion), coeficiente de determinación (R2) y la suma de cuadrados del error (SCE). El modelo más adecuado para todas las características fue aquel que involucró el GC y edad como efecto fijo para P4M y edad como covariable para PD.

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