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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
A Stochastic Version Of The Em Algorithm To Analyze Multivariate Skew-Normal Data With Missing Responses, M. Khounsiavash, M. Ganjali, T. Baghfalaki
A Stochastic Version Of The Em Algorithm To Analyze Multivariate Skew-Normal Data With Missing Responses, M. Khounsiavash, M. Ganjali, T. Baghfalaki
Applications and Applied Mathematics: An International Journal (AAM)
In this paper an algorithm called SEM, which is a stochastic version of the EM algorithm, is used to analyze multivariate skew-normal data with intermittent missing values. Also, a multivariate selection model framework for modeling of both missing and response mechanisms is formulated. By the SEM algorithm missing values of responses are inputed by the conditional distribution of missing values given observed data and then the log-likelihood of the pseudocomplete data is maximized. The algorithm is iterated until convergence of parameter estimates. Results of an application are also reported where a Bootstrap approach is used to compute the standard error …
Modeling Repairable System Failures With Interval Failure Data And Time Dependent Covariate, Jayanthi Arasan, Samira Ehsani
Modeling Repairable System Failures With Interval Failure Data And Time Dependent Covariate, Jayanthi Arasan, Samira Ehsani
Journal of Modern Applied Statistical Methods
An application of a repairable system model for interval failure data with a time dependent covariate is examined. The performance of several models based on the NHPP when applied to real data on ball bearing failures is also explored. The best model for the data was selected based on results of the likelihood ratio test. The bootstrapping technique was applied to obtain the variance estimate for the estimated expected number of failures. Results demonstrate that the proposed model works well and is easy to implement, in addition the bootstrap variance estimate provides a simple substitute for the traditional estimate.
Weighting Large Datasets With Complex Sampling Designs: Choosing The Appropriate Variance Estimation Method, Sara Mann, James Chowhan
Weighting Large Datasets With Complex Sampling Designs: Choosing The Appropriate Variance Estimation Method, Sara Mann, James Chowhan
Journal of Modern Applied Statistical Methods
Using the Canadian Workplace and Employee Survey (WES), three variance estimation methods for weighting large datasets with complex sampling designs are compared: simple final weighting, standard bootstrapping and mean bootstrapping. Using a logit analysis, it is shown - depending on which weighting method is used - different predictor variables are significant. The potential lack of independence inherent in a multi-stage cluster sample design, as in the WES, results in a downward bias in the variance when conducting statistical inference (using the simple final weight), which in turn results in increased Type I errors. Bootstrap methods can account for the survey’s …