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The maximum likelihood estimator (MLE) is commonly used to estimate the parameters of logistic regression models due to its efficiency under a parametric model. However, evidence has shown the MLE has an unduly effect on the parameter estimates in the presence of outliers. Robust methods are put forward to rectify this problem. This article examines the performance of the MLE and four existing robust estimators under different outlier patterns, which are investigated by real data sets and Monte Carlo simulation.

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ABSTRACT:The existence of missing values in rainfall data series is inevitably affects the quality of the data. This problem will influence the results of analysis and subsequently provide imprecise information to the hydrological and meteorological management. A practical and reliable approach is needed in developing estimation methods to impute the missing values. Single imputation is the most commonly used approach for missing values, but, it encounters with the limitation of not considering the uncertainty and natural variability in missing data imputation. Thus, this study has proposed multiple imputation approach based on bootstrap samples in order to overcome the limitation of single imputation approach. Three normal ratio estimation methods are implemented using the proposed approach. The performances of the estimation methods are evaluated at six different levels of missingness. Complete 40 years daily rainfall data from four meteorology stations were considered for the analysis purpose with Johor Bahru station was selected as the target station. The results of the proposed approach were compared to the results obtained from single imputation approach and the widely known built in software for multiple imputation, Amelia II package, in assessing the performance of proposed approach. The results showed that all estimation methods that implemented using proposed approach provided the most accurate estimation results at all percentages of missingness. This proves the advantage of adaption of variability and uncertainty element in the proposed approach in estimating the missing rainfall data at the area of the current study.

The main objective of this article is to design a compact ring printed monopole antenna for WiBAN applications. The performance of antenna is measured based on gain, return loss, efficiency, radiation pattern, and operating bandwidth. The antenna is implanted into canola oil (for homogeneous material) and beef (for nonhomogeneous material). The final measured result shows satisfactory performance and good agreement with the simulated result. The result presented indicates that the gain of antenna is 0.8 dBi with 49% efficiency when antenna is implanted into canola oil, whereas the gain of antenna is −30 dBi with 0.04% efficiency when antenna is implanted into beef. The radiation patterns of antenna are changed depend on the size and material of human body model. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1053–1058, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27503