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.
Baghfalaki,T. , Ganjali,M. and Khounsiavash,M. (2022). A Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response. Journal of the Iranian Statistical Society, 11(2), 101-129.
MLA
Baghfalaki,T. , , Ganjali,M. , and Khounsiavash,M. . "A Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response", Journal of the Iranian Statistical Society, 11, 2, 2022, 101-129.
HARVARD
Baghfalaki T., Ganjali M., Khounsiavash M. (2022). 'A Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response', Journal of the Iranian Statistical Society, 11(2), pp. 101-129.
CHICAGO
T. Baghfalaki, M. Ganjali and M. Khounsiavash, "A Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response," Journal of the Iranian Statistical Society, 11 2 (2022): 101-129,
VANCOUVER
Baghfalaki T., Ganjali M., Khounsiavash M. A Non-Random Dropout Model for Analyzing Longitudinal Skew-Normal Response. JIRSS, 2022; 11(2): 101-129.
Journal of the Iranian Statistical Society