Social Avoidance and stigma among healthcare workers who deal with Coronavirus COVID-19 Patients in Saudi Arabia

An online questionnaire was distributed to the target population (N = ~2000); 226 completed forms were received from respondents Missing values in all variables did not exceed 6% of cases. Missing data analysis was then followed with Little’s (1988) missing completely at random test. The results were not significant, χ2 (59) = 73.340, p = .099, suggesting that the values were missing entirely by chance. Thus, the missing values in the dataset were estimated with the expectation–maximization algorithm. To examine outliers among cases, data were evaluated for univariate and multivariate outliers by examining Mahalanobis distance for each participant. An outlier was defined as a Mahalanobis score that was over than Mahal. Critical score cv = 55.32; univariate or multivariate outliers were 31 cases with 13% (Tabachnik & Fidell, 2013, McLachlan GJ. (1999).

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Social Avoidance and stigma among healthcare workers who deal with Coronavirus COVID-19 Patients in Saudi Arabia

10.31124/advance.14237312 ◽

2021 ◽

Author(s):

Abduruhman Fahad Alajmi1 ◽

Hmoud Al-Olimat ◽

Reham Abu Ghaboush ◽

Nada A. Al Buniaian

Keyword(s):

Expectation Maximization ◽

Healthcare Workers ◽

Missing Values ◽

Expectation Maximization Algorithm ◽

Target Population ◽

Social Avoidance ◽

Online Questionnaire ◽

Missing Data Analysis ◽

Missing Completely At Random ◽

Multivariate Outliers

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Enhancing Data Quality at ETL Stage of Data Warehousing

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2021010105 ◽

2021 ◽

Vol 17 (1) ◽

pp. 74-91

Author(s):

Neha Gupta ◽

Sakshi Jolly

Keyword(s):

Data Quality ◽

Data Warehouse ◽

Expectation Maximization ◽

Euclidean Distance ◽

Missing Values ◽

Expectation Maximization Algorithm ◽

Distance Metrics ◽

Multiple Sources ◽

Minkowski Distance ◽

Data Quality Management

Data usually comes into data warehouses from multiple sources having different formats and are specifically categorized into three groups (i.e., structured, semi-structured, and unstructured). Various data mining technologies are used to collect, refine, and analyze the data which further leads to the problem of data quality management. Data purgation occurs when the data is subject to ETL methodology in order to maintain and improve the data quality. The data may contain unnecessary information and may have inappropriate symbols which can be defined as dummy values, cryptic values, or missing values. The present work has improved the expectation-maximization algorithm with dot product to handle cryptic data, DBSCAN method with Gower metrics to ensure dummy values, Wards algorithm with Minkowski distance to improve the results of contradicting data and K-means algorithm along with Euclidean distance metrics to handle missing values in a dataset. These distance metrics have improved the data quality and also helped in providing consistent data to be loaded into a data warehouse.

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Imputing missing distances in molecular phylogenetics

10.1101/276345 ◽

2018 ◽

Author(s):

Xuhua Xia

Keyword(s):

Missing Data ◽

Least Squares ◽

Molecular Clock ◽

Expectation Maximization ◽

Molecular Phylogenetics ◽

Missing Values ◽

Expectation Maximization Algorithm ◽

Distance Matrix ◽

Minimum Evolution ◽

Least Squares Approach

AbstractMissing data are frequently encountered in molecular phylogenetics and need to be imputed. For a distance matrix with missing distances, the least-squares approach is often used for imputing the missing values. Here I develop a method, similar to the expectation-maximization algorithm, to impute multiple missing distance in a distance matrix. I show that, for inferring the best tree and missing distances, the minimum evolution criterion is not as desirable as the least-squares criterion. I also discuss the problem involving cases where the missing values cannot be uniquely determined, e.g., when a missing distance involve two sister taxa. The new method has the advantage over the existing one in that it does not assume a molecular clock. I have implemented the function in DAMBE software which is freely available at available at http://dambe.bio.uottawa.ca

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Application of the Expectation Maximization Algorithm to Estimate Missing Values in Gaussian Bayesian Network Modeling for Forest Growth

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2011.2168823 ◽

2012 ◽

Vol 50 (5) ◽

pp. 1821-1831 ◽

Cited By ~ 6

Author(s):

Yaseen T. Mustafa ◽

Valentyn A. Tolpekin ◽

Alfred Stein

Keyword(s):

Bayesian Network ◽

Expectation Maximization ◽

Missing Values ◽

Network Modeling ◽

Expectation Maximization Algorithm ◽

Forest Growth ◽

Bayesian Network Modeling ◽

Gaussian Bayesian Network

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Early Fetal Weight Estimation with Expectation Maximization Algorithm

10.20944/preprints201803.0212.v1 ◽

2018 ◽

Author(s):

Loc Nguyen ◽

Thu-Hang T. Ho

Keyword(s):

Expectation Maximization ◽

Missing Values ◽

Expectation Maximization Algorithm ◽

Fetal Weight ◽

Abdominal Circumference ◽

Weight Estimation ◽

Time Points ◽

Fetal Weight Estimation ◽

Gestational Period ◽

Dual Regression

Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation but the regression model requires that time points of collecting such measures must not be too far from last ultrasound scans. Therefore this research proposes a method of early weight estimation based on expectation maximization (EM) algorithm so that ultrasound measures can be taken at any time points in gestational period. In other words, gestational sample can lack some or many fetus weights, which gives facilities to practitioners because practitioners need not concern fetus weights when taking ultrasound examinations. The proposed method is called dual regression expectation maximization (DREM) algorithm. Experimental results indicate that accuracy of DREM decreases insignificantly when completion of ultrasound sample decreases significantly. So it is proved that DREM withstands missing values in incomplete sample or sparse sample.

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