An investigation of several typical model selection criteria for detecting the number of signals

Based on the problem of detecting the number of signals,this paper provides a systematic empirical investigation on model selection performances of several classical criteria and recently developed methods(including Akaike’s information criterion(AIC),Schwarz’s Bayesian information criterion,Bozdogan’s consistent AIC,Hannan-Quinn information criterion,Minka’s(MK)principal component analysis(PCA)criterion,Kritchman&Nadler’s hypothesis tests(KN),Perry&Wolfe’s minimax rank estimation thresholding algorithm(MM),and Bayesian Ying-Yang(BYY)harmony learning),by varying signal-to-noise ratio(SNR)and training sample size N.A family of model selection indifference curves is defined by the contour lines of model selection accuracies,such that we can examine the joint effect of N and SNR rather than merely the effect of either of SNR and N with the other fixed as usually done in the literature.The indifference curves visually reveal that all methods demonstrate relative advantages obviously within a region of moderate N and SNR.Moreover,the importance of studying this region is also confirmed by an alternative reference criterion by maximizing the testing likelihood.It has been shown via extensive simulations that AIC and BYY harmony learning,as well as MK,KN,and MM,are relatively more robust than the others against decreasing N and SNR,and BYY is superior for a small sample size.

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

Predictive modeling of COVID-19 death cases in Pakistan

Background:The world is presently facing the challenges posed by COVID-19(2019-nCoV),especially in the public health sector,and these challenges are dangerous to both health and life.The disease results in an acute respiratory infection that may result in pain and death.In Pakistan,the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5,2020.Objective:The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan.The age and gender of COVID-19 victims were represented using a descriptive study.Method:ology:Three regression models,which include Linear,logarithmic,and quadratic,were employed in this study for the modelling of COVID-19 death cases in Pakistan.These three models were compared based on R2,Adjusted R2,AIC,and BIC criterions.The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26,2020 to August 5,2020.Conclusion:The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October.The total number of deaths will reach its maximum point;then,it will gradually decrease.This indicates that the curve of total deaths will continue to be flat,i.e.,it will shift to be constant,which is also the upper bound of the underlying function of absolute death.