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Goodness of fit test for general linear model with nonignorable missing on response variable

Author

Listed:
  • Fayyaz Bahari

    (University of Mohaghegh Ardabili)

  • Safar Parsi

    (University of Mohaghegh Ardabili)

  • Mojtaba Ganjali

    (Shahid Beheshti University)

Abstract

In this paper, we consider a general linear model where missing data occur in the response variable with a nonignorable mechanism. Also, to deal with missing data, we assume that the probability of missing data follows a logistic model. The main purpose of this paper is to construct some test functions to check the goodness of fit of the general linear model based on the score-type test. To achieve this aim, we use two appropriate estimating models and we construct two test functions based on these models. The asymptotic properties of the test functions are obtained under the null and the alternative hypotheses based on the estimated tilting parameter. The performances of the test functions are checked by some simulation studies. Also, these methods are used to check goodness of fit of the fitted models for real data.

Suggested Citation

  • Fayyaz Bahari & Safar Parsi & Mojtaba Ganjali, 2021. "Goodness of fit test for general linear model with nonignorable missing on response variable," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 163-196, March.
    • Handle: RePEc:spr:alstar:v:105:y:2021:i:1:d:10.1007_s10182-020-00367-4
    DOI: 10.1007/s10182-020-00367-4
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    References listed on IDEAS

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    1. Wang, Suojin & Wang, C. Y., 2001. "A note on kernel assisted estimators in missing covariate regression," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 439-449, December.
    2. Kim, Jae Kwang & Yu, Cindy Long, 2011. "A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 157-165.
    3. Sun, Zhihua & Wang, Qihua & Dai, Pengjie, 2009. "Model checking for partially linear models with missing responses at random," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 636-651, April.
    4. Zhao, Hui & Zhao, Pu-Ying & Tang, Nian-Sheng, 2013. "Empirical likelihood inference for mean functionals with nonignorably missing response data," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 101-116.
    5. Minna Genbäck & Elena Stanghellini & Xavier Luna, 2015. "Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome," Statistical Papers, Springer, vol. 56(3), pages 829-847, August.
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