Allison McClure (flaregirl96)

Traditional data envelopment analysis (DEA) models assume that all the inputs and outputs data are available. However, missing data is a common problem in data analysis. Although several scholars have developed techniques to conduct DEA with missing data, these techniques have some disadvantages. A multi-criteria evaluation approach is proposed to measure the efficiency of decision making units (DMUs) with missing data. In this approach, analysts first estimate the upper and lower bounds of DMUs' efficiency using the proposed I-addIDEA-U models (interval additive integer-valued DEA models with undesirable outputs) that can be applied to address integer-valued variables and undesirable outputs. Then, DMUs' "relative" efficiency is evaluated using the proposed "Halo + Hot deck" DEA method (if there is no correlation between variables) or regression DEA techniques (if there is a correlation between variables). Finally, the multi-index comprehensive evaluation method is applied to determine which scenario (the lower bound of efficiency, the "relative" efficiency, or the upper bound of efficiency) should be selected. With a case study, it is shown that the proposed multi-criteria evaluation approach is more effective than traditional approaches such as the mean imputation DEA method, the deletion DEA method, and the dummy entries DEA method.Statistical inference in the form of hypothesis tests and confidence intervals often assumes that the underlying distribution is normal. Similarly, many signal processing techniques rely on the assumption that a stationary time series is normal. As a result, a number of tests have been proposed in the literature for detecting departures from normality. In this article we develop a novel approach to the problem of testing normality by constructing a statistical test based on the Edgeworth expansion, which approximates a probability distribution in terms of its cumulants. By modifying one term of the expansion, we define a test statistic which includes information on the first four moments. We perform a comparison of the proposed test with existing tests for normality by analyzing different platykurtic and leptokurtic distributions including generalized Gaussian, mixed Gaussian, α-stable and Student's t distributions. We show for some considered sample sizes that the proposed test is superior in terms of power for the platykurtic distributions whereas for the leptokurtic ones it is close to the best tests like those of D'Agostino-Pearson, Jarque-Bera and Shapiro-Wilk. Finally, we study two real data examples which illustrate the efficacy of the proposed test.Background The first case of HIV infection in Sri Lanka was reported in 1987 and at the end of 2018 there were 3500 people living with HIV. There have been commendable efforts made towards the detection, treatment, and prevention of HIV in the country. Even though the genetic diversity of HIV has been shown to affect the parameters ranging from detection to vaccine development, there is no data available with respect to the molecular epidemiology of HIV-1 in Sri Lanka. Methods In this report we have performed the ancillary analysis of pol gene region sequences (n = 85) obtained primarily for the purpose of HIV-1 drug resistance genotyping. Briefly, dried blood spot specimens (DBS) collected from HIV-1 infected individuals between December 2015 and August 2018 were subjected to pol gene amplification and sequencing. These pol gene sequences were used to interpret the drug resistance mutation profiles. Further, sequences were subjected to HIV-1 subtyping using REGA 3.0, COMET, jPHMM and, RIP online subtyping tomolecular surveillance of HIV-1 molecular epidemiology will be crucial to keep track of drug resistance, genetic diversity, and evolutionary history of HIV-1 in Sri Lanka.Approximately 20% of breast cancers are HER2-positive. Trastuzumab has improved patient outcomes significantly