A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

Financial development during COVID‑19 pandemic:the role of coronavirus testing and functional labs

The outbreak of the SARS-CoV-2 virus in early 2020,known as COVID-19,spread to more than 200 countries and negatively affected the global economic output.Financial activities were primarily depressed,and investors were reluctant to start new financial investments while ongoing projects further declined due to the global lockdown to curb the disease.This study analyzes the money supply reaction to the COVID-19 pandemic using a cross-sectional panel of 115 countries.The study used robust least square regression and innovation accounting techniques to get sound parameter estimates.The results show that COVID-19 infected cases are the main contributing factor that obstructs financial activities and decrease money supply.In contrast,an increasing number of recovered cases and COVID-19 testing capabilities gave investors confidence to increase stock trade across countries.The overall forecast trend shows that COVID-19 infected cases and recovered cases followed the U-shaped trend,while COVID-19 critical cases and reported deaths showed a decreasing trend.Finally,the money supply and testing capacity show a positive trend over a period.The study concludes that financial development can be expanded by increasing the testing capacity and functional labs to identify suspected coronavirus cases globally.