Volume 11, Issue 2 (November 2012)                   JIRSS 2012, 11(2): 101-129 | Back to browse issues page

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Baghfalaki T, Ganjali M, Khounsiavash M. A Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response. JIRSS. 2012; 11 (2) :101-129
URL: http://jirss.irstat.ir/article-1-188-en.html
Abstract:   (8475 Views)
In this paper, multivariate skew-normal distribution is em- ployed for analyzing an outcome based dropout model for repeated mea- surements with non-random dropout in skew regression data sets. A probit regression is considered as the conditional probability of an ob- servation to be missing given outcomes. A simulation study of using the proposed methodology and comparing it with a semi-parametric method, GEE, is provided. The standardized bias is used for compari- son of different approaches. Furthermore, for investigation of efficiency of the methodology two applications are analyzed where observed infor- mation matrix is used to find the variances of the parameter estimates. In one of the applications a sensitivity analysis is also performed to in- vestigate the change on the response model’s parameter estimates due to perturbation of drop-out model’s parameter of interest.
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Received: 2012/10/11 | Accepted: 2015/09/12 | Published: 2012/11/15

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