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Analysis of correlated Birnbaum–Saunders data based on estimating equations

  • Aline B. Tsuyuguchi [1] ; Gilberto A. Paula [2] ; Michelli Barros [3]
    1. [1] Universidade Federal de Pernambuco

      Universidade Federal de Pernambuco

      Brasil

    2. [2] Universidade de São Paulo

      Universidade de São Paulo

      Brasil

    3. [3] Universidade Federal de Campina Grande

      Universidade Federal de Campina Grande

      Brasil

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  • Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 29, Nº. 3, 2020, págs. 661-681
  • Idioma: inglés
  • DOI: 10.1007/s11749-019-00675-1
  • Texto completo no disponible (Saber más ...)
  • Resumen

    • Estimating equations for analyzing correlated Birnbaum–Saunders (BS) data are derived in this paper. A regression model is proposed for modeling the median of the life time until the failure, and a reweighted iterative process is developed for the joint estimation of the regression coefficients and the shape and correlation parameters. Diagnostic procedures, such as residual analysis and sensitivity studies based on case deletion and local influence, are given. Simulation studies are performed to assess the empirical distributions of the derived estimators and of a Pearson-type residual for correlated data. Finally, a longitudinal data set is analyzed by the procedures developed in the paper and extensions for the double case in which the median and the shape parameter are jointly modeled are discussed.

  • Referencias bibliográficas
    • Alencar AP, Singer JM, Rocha FMM (2012) Competing regression models for longitudinal data. Biom J 54:214–229
    • Artes R (1997) Extensions of the Generalized Estimating Equation Theory to Circular Data and Dispersion Models [unpublished Ph.D. thesis in...
    • Artes R, Jorgensen B (2000) Longitudinal data estimating equations for dispersion model. Scand J Stat 27:321–334
    • Atkinson AC (1981) Two graphical displays for outlying and influential observations in regression. Biometrika 68:13–20
    • Balakrishnan N, Kundu D (2019) Birnbaum-Saunders distribution: a review of models, analysis, and applications. Appl Stoch Model Bus 35:4–49
    • Birnbaum ZW, Saunders SC (1969) A new family of life distributions. J Appl Probab 6:319–327
    • Cadigan NG, Farrell PJ (2002) Generalized local influence with applications to fish stock cohort analysis. Appl Stat 51:469–483
    • Cook RD (1977) Detection of influential observations in linear regressions. Technometrics 19:15–18
    • Cook RD (1986) Assessment of local influence. J R Stat Soc B 48:133–169
    • Crowder M (1987) On linear and quadratic estimating functions. Biometrika 74:591–597
    • Fitzmaurice GM, Laird NM, Rotnitzky AG (1993) Regression models for discrete longitudinal responses. Stat Sci 8:284–299
    • Godambe VP (1997) Estimating functions: a synthesis of least squares and maximum likelihood methods. In: Selected proceedings of the symposium...
    • Greene WH (2012) Econometric analysis. Prentice Hall, New York
    • Hardin JW, Hilbe JM (2012) Generalized estimating equations. Chapman & Hall, Cambridge
    • Jorgensen B, Lundbye-Christensen S, Song PX-K, Sun L (1996) State-space models for multivariate longitudinal data of mixed types. Can J Stat...
    • Kim SK, Huggins R (1998) Diagnostic for autocorrelation regression models. Aust N Z J Stat 40:65–71
    • Kundu D, Balakrishnan N, Jamalizadeh A (2013) Generalized multivariate Birnbaum–Saunders distributions and related inferential results. J...
    • Leiva V (2016) The Birnbaum–Saunders distribution. Elsevier, Amsterdam
    • Leiva V, Barros M, Paula GA, Galea M (2007) Influence diagnostics in log-Birnbaum–Saunders regression models with censored data. Comput Stat...
    • Liang KY, Zeger SL (1986) Longitudinal analysis using generalized linear models. Biometrika 73:13–22
    • Liang NY, Zeger SL, Qaqish (1992) Multivariate regression analyses for categorical data. J R Stat Soc B 54:3–40
    • Manghi RF, Paula GA, Cysneiros JFA (2016) On elliptical multilevel models. J Appl Stat 43:2150–2171
    • Marchant C, Leiva V, Cysneiros FJA (2016) A multivariate log-linear model for Birnbaum–Saunders distributions. IEEE Trans Reliab 652:816–827
    • Marchant C, Leiva V, Cysneiros FJA, Vivanco JF (2016) Diagnostics in multivariate Birnbaum–Saunders regression models. J Appl Stat 43:2829–2849
    • Moral RA, Hinde J, Demétrio CGB (2017) Half-normal plots and overdispersed models in R: the hnp package. J Stat Softw 81:1–23
    • Munnell AH (1990) Why has productivity declined? Productivity and public investment. N Engl Econ Rev Jan/Feb 3–22
    • Paula GA, Medeiros M, Vilca-Labra FE (2009) Influence diagnostics for linear models with first-order autoregressive elliptical errors. Stat...
    • Poon W, Poon YS (1999) Conformal normal curvature and assessment of local influence. J R Stat Soc B 61:51–61
    • Pregibon D (1981) Logistic regression diagnostics. Ann Stat 9:705–724
    • Preisser JS, Qaqish BF (1996) Deletion diagnostics for generalised estimating equations. Biometrika 83:551–562
    • R Core Team (2017) R: a language and environment for statistical computing. https://www.R-project.org/
    • Rieck JR, Nedelman JR (1991) A log-linear model for the Birnbaum–Saunders distribution. Technometrics 33:51–60
    • Venezuela MK, Botter DA, Sandoval MC (2007) Diagnostic techniques in generalized estimating equations. J Stat Comput Simul 77:879–888
    • Venezuela MK, Sandoval MC, Botter DA (2011) Local influence in estimating equations. Comput Stat Data Anal 55:1867–1883
    • Villegas C, Paula GA, Leiva V (2011) Birnbaum–Saunders mixed models for censored reliability data analysis. IEEE Trans Reliab 60:748–758
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