scholarly journals Previsão da temperatura média mensal de Uberlândia, MG, com modelos de séries temporais

2008 ◽  
Vol 12 (5) ◽  
pp. 480-485 ◽  
Author(s):  
Maria I. S. Silva ◽  
Ednaldo C. Guimarães ◽  
Marcelo Tavares
Keyword(s):  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Mean Square ◽  
De Se

Modelos de séries temporais têm sido amplamente usados no estudo de variáveis climatológicas, como temperatura e precipitação. Diversos são os objetivos traçados neste trabalho a fim de se analisar a série de temperatura média mensal da cidade de Uberlândia, MG, descrevendo seus componentes, e fazer previsões para períodos subseqüentes através de modelos ajustados para a série. A análise permitiu identificar, na série, a presença dos componentes, tendência e sazonalidade. Modelos do tipo SARIMA foram ajustados e, por meio dos critérios AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion) e MSE (Mean Square Error) foi selecionado o modelo SARIMA (3,1,0)(0,1,1) para fins de previsão.

scholarly journals Modeling of COVID-19 Outbreak Indicators in China Between January and June

2020 ◽  
pp. 1-9
Author(s):  
Senol Celik ◽  
Handan Ankarali ◽  
Ozge Pasin
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Linear Trend ◽  
Growth Curves ◽  
Information Criterion ◽  
Time Series Models ◽  
Cumulative Number ◽  
Mean Square ◽  
Nonlinear Growth

ABSTRACT Objectives: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. Methods: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. Results: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. Conclusion: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

scholarly journals EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PENCILAN PONTENSIAL

2020 ◽  
Vol 14 (2) ◽  
pp. 305-312
Author(s):  
Netti Herawati
Keyword(s):  
Quantile Regression ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Robust Regression ◽  
Information Criterion ◽  
Regression Method ◽  
Least Square ◽  
Mean Square ◽  
The Impact ◽  
Quantile Regression Method

Abstrak Regresi kuantil sebagai metode regresi yang robust dapat digunakan untuk mengatasi dampak kasus yang tidak biasa pada estimasi regresi. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas regresi kuantil untuk menangani pencilan potensial dalam regresi linear berganda dibandingkan dengan metode kuadrat terkecil (MKT). Penelitian ini menggunakan data simulasi dengan p=3; n = 20, 40, 60, 100, 200 and   and  diulang 1000 kali. Efektivitas metode regresi kuantil dan MKT dalam pendugaan parameter β diukur dengan Mean square error (MSE) dan Akaike Information Criterion (AIC). Hasil penelitian menunjukkan bahwa regresi kuantil mampu menangani pencilan potensial dan memberikan penaksir yang lebih baik dibandingkan dengan MKT berdasarkan nilai MSE dan AIC. Kata kunci: AIC, MSE, pencilan, regresi kuantil Abstract Quantitative regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimation. The purpose of this study is to evaluate the effectiveness of quantile regression to deal with potential outliers in multiple linear regression compared to the least squares methodordinary least square (OLS).   This study uses simulation data with p=3; n = 20, 40, 60, 100, 200 and   and  repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β   parameters was measured by Mean square error (MSE) and Akaike Information Criterion (AIC). The results showed that quantile regression was able to handle potential outliers and provide better predictors compared to MKT based on MSE and AIC values. Keywords: AIC, MSE, outliers, quantile regression

scholarly journals Sinusoidal function to describe the growth curve of dairy heifers

2019 ◽  
Vol 59 (6) ◽  
pp. 1039
Author(s):  
H. Darmani Kuhi ◽  
N. Ghavi Hossein-Zadeh ◽  
S. López ◽  
S. Falahi ◽  
J. France
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Dairy Heifers ◽  
Growth Functions ◽  
Best Fit ◽  
Sinusoidal Function

The objective of the present study is to introduce a sinusoidal function into dairy research and production by applying it to bodyweight records (from 1 to 24 months) from six dairy cow breeds reported by the Dairy Heifer Evaluation Project of Penn State Extension (USA) from 1991 to 1992. The function was evaluated with regard to its ability to describe the relationship between bodyweight and age in dairy heifers, and then compared with seven standard growth functions, namely monomolecular, logistic, Gompertz, von Bertalanffy, Richards, Schumacher and Morgan. The models were fitted to monthly bodyweight records of dairy heifers using non-linear regression to derive estimates of the parameters of each function. The models were tested for goodness of fit by using adjusted coefficient of determination, root mean square error, Akaike’s information criterion and Bayesian information criterion. Values of adjusted coefficient of determination were generally high for all models, suggesting the generally appropriate fit of the models to the data. The sinusoidal function provided the best fit of the growth curves for Brown Swiss, Guernsey and Milking Shorthorn breeds due to the lowest values of root mean square error, Akaike’s information criterion and Bayesian information criterion. According to the chosen statistical criteria, the Richards function provided the best fit for Ayrshire heifers, and the monomolecular the best for Holstein and Jersey. The least accurate estimates were obtained with the logistic. In conclusion, the sinusoidal function introduced here can be considered as an appropriate alternative to standard growth functions when modelling growth patterns in dairy heifers.

scholarly journals Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model for the State of Tamilnadu, India

2020 ◽  
Author(s):  
Prasannavenkatesan Theerthagiri
Keyword(s):  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Virus Disease ◽  
Research Work ◽  
Information Criterion ◽  
Mean Square ◽  
Error Metrics ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Performance Error

Abstract The world has been struck due to the dangerous human threat called Corona Virus Disease 2019. This research work proposes a methodology to encounter the future infection rate, curing rate, and decease rate. This uses the artificial intelligence algorithm to design and develop the proposed confirmed, cured, deceased (COCUDE) model. A machine learning model has been developed with several iterations to design the proposed COCUDE model. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Correlated Akaike Information criterion (AICc) metrics are analyzed to check the stationary and quality for the proposed COCUDE model. The prediction results are evaluated by the performance error metrics such as mean square error (MSE) and root mean square error (RMSE), in which the errors are lower for the proposed model. Thus, the prediction results indicate the proposed COCUDE model might accurately predict future COVID-19 infection rates. It might support the corresponding authorities to take the precautious action on the required necessities for the medical and clinical infrastructures and equipment.

scholarly journals Validação da estrutura fatorial do Körperkoordination Test für Kinder (KTK) em escolares de 8 a 10 anos

2020 ◽  
Vol 8 (3) ◽  
pp. 31
Author(s):  
João Otacilio Libardoni Dos Santos ◽  
Pâmella De Medeiros ◽  
Fernando Luiz Cardoso ◽  
Nilton Soares Formiga ◽  
Nayara Christine Souza ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Goodness Of Fit ◽  
Cross Validation ◽  
Information Criterion ◽  
Mean Square ◽  
Fit Index ◽  
Close Fit

Objetivo: Avaliar a estrutura fatorial do teste KTK em crianças em idade escolar, na faixa etária entre 8 e 10 anos, com base na estrutura unifatorial do KTK. Método: Foram avaliados 350 escolares da cidade de Manaus-AM com idade entre 8 e 10 anos de ambos os sexos. Para análise dos dados considerou-se como entrada, a matriz de covariância, tendo sido adotado o estimador ML (Maximum-Likelihood). Foram utilizados os seguintes indicadores: χ²/gl (qui-quadrado e grau de liberdade), Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Root-Mean-Square Error of Approximation (RMSEA), p de Close Fit (PCLOSE), Comparative Fit Index (CFI), Expected Cross-Validation Index (ECVI) e o Consistent Akaike Information Criterion (CAIC). Resultados: A análise fatorial confirmou o modelo unifatorial original da bateria de testes. Deixando livre as covariâncias (phi, φ) entre os itens, os resultados revelaram que os indicadores de qualidade de ajuste são aceitáveis para o modelo proposto, o qual é composto por quatro itens distribuídos em um único fator (χ²/gl = 1.09; GFI = 0.99; AGFI = 0.94; CFI = 0.97; TLI = 0.92; RMSEA = 0.07; PCLOSE = 0.10). Observou-se ainda que todas as saturações (Lambdas, λ), tanto estiveram dentro do intervalo esperado |0 - 1| quando foram estatisticamente diferentes de zero (t > 1.96, p < 0.05). Conclusões: Foi possível identificar aceitáveis evidências de validade baseada na estrutura interna do KTK proposto pelos autores originais confirmando a sua capacidade de investigar e classificar o nível de coordenação motora de crianças, identificando possíveis perturbações ou insuficiências na população avaliada.

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 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.

scholarly journals STATISTICAL INTERPRETATION AND OPTIMIZATION OF VALSARTAN FLOATING TABLETS USING BOX-BEHNKEN DESIGN

2019 ◽  
pp. 44-53
Author(s):  
SANKHA BHATTACHARYA
Keyword(s):  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Stability Study ◽  
Therapeutic Window ◽  
Dissolution Time ◽  
Floating Tablets ◽  
Mean Dissolution Time ◽  
Styrene Divinylbenzene

Objective: The main purpose of this study was to formulate and statistically evaluate 300 mg floating tablets of valsartan. Methods: Floating tablets of valsartan was prepared in 16 station rotary punching machine by considering 300 mg of valsartan as drug, 40-60 mg of hydroxypropyl methylcellulose (HPMC) K100M and 20-40 mg of poly (styrene-divinylbenzene) as polymers and 20 mg of sodium bicarbonate as gas generating agents. Since upper stomach has maximum therapeutic window for valsartan absorption, hence Gastroretentive Floating Tablets (GRFTs) was prepared by implementing Box-Bentham Design. The pre and post compression parameters were optimized using Statistica 10 software. From the in vitro buoyancy and drug release studies and interpretation of statistical outcomes viz. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), Dissolution Efficiency (DE), Mean Dissolution Time (MDT), desirability study, it was concluded that batch VF5 formulation was found to be the most optimized formulation. Results: The floating time of VF5 was found to be 132±0.33 sec, in vitro buoyancy time was 18 h, Akaike Information Criterion (AIC) was 54.97, Bayesian Information Criterion (BIC) was 5.13, percentage dissolution efficacy was 56.39%, mean dissolution time was 5.19hr. Further, six-month stability study was performed as per ICH QIA guideline. After performing two-way ANOVA within stability study response variables, it was confirmed that the interaction was most significant. Conclusion: Valsartan floating drug delivery system was successfully developed by considering HPMC K100M and poly (styrene-divinylbenzene) as polymers. Among all the nine batches, VF5 was found to be the best-optimized batch.

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