The dual response surface for simultaneously optimizing the mean and variance models as separate functions suffers some deficiencies in handling the tradeoffs between bias and variance components of mean squared error (MSE). In this paper, the accuracy of the predicted response is given a serious attention in the determination of the optimum setting conditions. We consider four different objective functions for the dual response surface optimization approach. The essence of the proposed method is to reduce the influence of variance of the predicted response by minimizing the variability relative to the quality characteristics of interest and at the same time achieving the specific target output. The basic idea is to convert the constraint optimization function into an unconstraint problem by adding the constraint to the original objective function. Numerical examples and simulations study are carried out to compare performance of the proposed method with some existing procedures. Numerical results show that the performance of the proposed method is encouraging and has exhibited clear improvement over the existing approaches.

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Infant Mortality Rates (IMR) are important indicators of health status of any country. This research presents Time Series Analysis using Exponential Smoothing and Moving Averages (MA). Six years data on infant mortality covering 2016 to 2021 was obtained from Yobe State Specialist Hospital Damaturu. We used Single Exponential Smoothing, which at =2, the model showed a smoothed trend of infant mortality over the period of 72 months. A forecast made based on this smoothed trend indicated a constant rate of infant mortality over the period of 8 months with MAPE = 17.9165, MAD = 4.5133 and MSD = 30.2982. We also studied nature of trend using Moving Averages (MA) and forecast made at length = 4 showed a constant rate of forecast over the period of 8 months with MAPE = 19.3504, MAD = 4.8377, and MSD = 34.1556. Based on the three accuracy measures, single exponential smoothing method presented a better fit to the data. Highest infant mortality was observed in 2021 with total death of 331 which represents 17.08% of the total deaths.

The widely used least absolute deviation (LAD) estimator with the smoothly clipped absolute deviation (SCAD) penalty function (abbreviated as LAD-SCAD) is known to produce corrupt estimates in the presence of outlying observations. The problem becomes more complicated when the number of predictors diverges. To overcome these problems, the LAD-SCAD based on sure independence screening (SIS) technique is put forward. The SIS method uses the rank correlation screening (RCS) algorithm in the pre-screening step and the traditional Pathwise coordinate descent algorithm for computing the sequence of the regularization parameters in the post screening step for onward model selection. It is now evident that the rank correlation is less robust against outliers. Motivated by these inadequacies, we propose to improvise the LAD-SCAD estimator using robust wrapped correlation screening (WCS) method by replacing the rank correlation in the SIS method with robust wrapped correlation. The proposed estimator is denoted as WCS+LAD-SCAD and will be employed for variable selection. The simulation study and real-life data examples show that the proposed procedure produces more efficient results compared to the existing methods.

The Lin and Tu (LT) optimization scheme which is based on mean squared error (MSE) objective function is the commonly used optimization scheme for estimating the optimal mean response in robust dual response surface optimization. The ordinary least squares (OLS) method is often used to estimate the parameters of the process location and process scale models of the responses. However, the OLS is not efficient for the unbalanced design data since this kind of data make the errors of a model become heteroscedastic, which produces large standard errors of the estimates. To remedy this problem, a weighted least squares (WLS) method is put forward. Since the LT optimization scheme produces a large difference between the estimates of the mean response and the experimenter actual target value, we propose a new optimization scheme. The OLS and the WLS are integrated in the proposed scheme to determine the optimal solution of the estimated responses. The results of the simulation study and real example indicate that the WLS is superior when compared with the OLS method irrespective of the optimization scheme used. However, the combination of WLS and the proposed optimization scheme (PFO) signify more efficient results when compared to the WLS combined with the LT optimization scheme.