Inference of Adaptive methods for Multi-Stage skew-t Simulated Data

  • Loai M. A. Al-Zou’bi Al al-Bayt University, Department of Mathematics, Mafraq , Jordan
  • Amer I. Al-Omari Al al-Bayt University, Department of Mathematics, Mafraq , Jordan
  • Ahmad M. Al-Khazalah Al al-Bayt University, Department of Mathematics, Mafraq , Jordan
  • Raed A. Alzghool Al-Balqa Applied University, Department of Mathematics, Salt, Jordan

Abstract

Multilevel models can be used to account for clustering in data from multi-stage surveys. In some cases, the intra-cluster correlation may be close to zero, so that it may seem reasonable to ignore clustering and fit a single level model. This article proposes several adaptive strategies for allowing for clustering in regression analysis of multi-stage survey data. The approach is based on testing whether the cluster-level variance component is zero. If this hypothesis is retained, then variance estimates are calculated ignoring clustering; otherwise, clustering is reflected in variance estimation. A simple simulation study is used to evaluate the various procedures.

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Published
2017-08-31
How to Cite
Al-Zou’bi, L. M. A., Al-Omari, A. I., Al-Khazalah, A. M., & Alzghool, R. A. (2017). Inference of Adaptive methods for Multi-Stage skew-t Simulated Data. European Scientific Journal, ESJ, 13(24), 448. https://doi.org/10.19044/esj.2017.v13n24p448