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Research paper | 13-December-2017

SYSTEM IDENTIFICATION OF NONLINEAR AUTOREGRESSIVE MODELS IN MONITORING DENGUE INFECTION

This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In building the model, three selection criteria, i.e. the final prediction error (FPE), Akaike’s Information Criteria (AIC), and Lipschitz number were used. Each of the models is divided into two approaches, which are unregularized approach and regularized approach. The findings indicate that NARMAX model with regularized approach yields

H. Abdul Rahim, F. Ibrahim, M. N. Taib

International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 4, 783–806

Research paper | 13-December-2017

MODEL ORDER SELECTION CRITERION FOR MONITORING HAEMOGLOBIN STATUS IN DENGUE PATIENTS USING ARMAX MODEL

This paper describes the development of linear autoregressive moving average with exogenous input (ARMAX) models to monitor the progression of dengue infection based on hemoglobin status. Three differents ARMAX model order selection criteria namely Final Prediction Error (FPE), Akaike’s Information Criteria (AIC) and Lipschitz number have been evaluated and analyzed. The results showed that Lipschitz number has better accuracy compared to FPE and AIC. Finally based on Lipschitz number

H. Abdul Rahim, F. Ibrahim, M. N. Taib, R. Abdul Rahim, Y.Mad Sam

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 2, 403–419

Research Article | 03-March-2021

Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach

aggregated variance and the Higuchi methods were applied to test for the presence of long memory in the dataset. Furthermore, four breaks have been detected: in 1986, 1999, 2005, and 2013 using the Bayes information criterion. In the further section of the paper, the Hurst Exponent and Geweke-Porter-Hudak (GPH) methods were used to estimate the values of fractional differences. Thus, some ARFIMA models were identified using AIC (Akaike Information Criterion), BIC (Schwartz Bayesian Information Criterion

Remal Shaher AlـGounmeein, Mohd Tahir Ismail

Statistics in Transition New Series, Volume 22 , ISSUE 1, 29–54

Article | 15-September-2020

Effective transformation-based variable selection under two-fold subarea models in small area estimation

Song Cai, J. N. K. Rao, Laura Dumitrescu, Golshid Chatrchi

Statistics in Transition New Series, Volume 21 , ISSUE 4, 68–83

Research Article | 03-March-2021

A latent class analysis on the usage of mobile phones among management students

Sunil Kumar, Apurba Vishal Dabgotra

Statistics in Transition New Series, Volume 22 , ISSUE 1, 89–114

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