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Bootstrapping estimate are exactly same across different runs in mplus is that correct?

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Cross-validation

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Bootstrapping estimates in Mplus may not be exactly the same across different runs due to the nature of bootstrapping methods. In nonparametric bootstrapping, the estimate of the population density function is derived from sampled observations, assuming the sample represents the population, leading to potential variability in estimates . Additionally, conditional parametric bootstrapping involves simulations where the estimator remains constant, providing exact confidence intervals but not necessarily identical estimates in each run . Moreover, a new block bootstrap method for recursive m-estimators introduces adjustments to mimic limiting distributions, indicating potential variations in estimates across runs . Therefore, while bootstrapping provides valuable estimates, exact replication of results across different runs may not always occur in Mplus due to inherent variability and adjustments in the methods.

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Open access•Posted Content•DOI Bootstrapping the Cross-Validation Estimate 01 Jul 2023	Not addressed in the paper.
Book Chapter•DOI Bootstrapping in Multilevel Models Harvey Goldstein 19 Jul 2010 29 Citations	Not addressed in the paper.
Bootstrapping the Cross-Validation Estimate Bryan Cai, Fabio Pellegrini, Menglan Pang, Chien-Yeh Shen, Vivek Charu, Lucy Tian - Show less +5 more 01 Jul 2023	Not addressed in the paper.
Journal Article•DOI Exact confidence intervals generated by conditional parametric bootstrapping Magnar Lillegård, Steinar Engen - Show less +1 more 01 May 1999-Journal of Applied Statistics 9 Citations	Not addressed in the paper.

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