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Muhammad Aslam1
Stephen W. Raudenbush1
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Generating Imprecise Data from Log-Normal Distribution
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Muhammad Aslam – Measurement: Interdisciplinary Research and Perspectives, 2025
The existing algorithm employing the log-normal distribution lacks applicability in generating imprecise data. This paper addresses this limitation by first introducing the log-normal distribution as a means to handle imprecise data. Subsequently, we leverage the neutrosophic log-normal distribution to devise an algorithm specifically tailored for…
Descriptors: Statistical Distributions, Algorithms, Sampling
Multiple Imputation to Estimate Hierarchical Models from Data Missing at Random: Latent Covariates, Random Coefficients, and Statistical Interactions
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Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2025
Consider the conventional multilevel model Y=C[gamma]+Zu+e where [gamma] represents fixed effects and (u,e) are multivariate normal random effects. The continuous outcomes Y and covariates C are fully observed with a subset Z of C. The parameters are [theta]=([gamma],var(u),var(e)). Dempster, Rubin and Tsutakawa (1981) framed the estimation as a…
Descriptors: Hierarchical Linear Modeling, Maximum Likelihood Statistics, Sampling, Error of Measurement