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Quasi-beta Longitudinal Regression Model Applied to Water Quality Index Data

Author

Listed:
  • Ricardo Rasmussen Petterle

    (Paraná Federal University)

  • Wagner Hugo Bonat

    (Paraná Federal University)

  • Cassius Tadeu Scarpin

    (Paraná Federal University (UFPR))

Abstract

We propose a new class of regression models to deal with longitudinal continuous bounded data. The model is specified using second-moment assumptions, and we employ an estimating function approach for parameter estimation and inference. The main advantage of the proposed approach is that it does not need to assume a multivariate probability distribution for the response vector. The fitting procedure is easily implemented using a simple and efficient Newton scoring algorithm. Thus, the quasi-beta longitudinal regression model can easily handle data in the unit interval, including exact zeros and ones. The covariance structure is defined in terms of a matrix linear predictor composed by known matrices. A simulation study was conducted to check the properties of the estimating function estimators of the regression and dispersion parameter estimators. The NORTA algorithm (NORmal To Anything) was used to simulate correlated beta random variables. The results of this simulation study showed that the estimators are consistent and unbiased for large samples. The model is motivated by a data set concerning the water quality index, whose goal is to investigate the effect of dams on the water quality index measured on power plant reservoirs. Furthermore, diagnostic techniques were adapted to the proposed models, such as DFFITS, DFBETAS, Cook’s distance and half-normal plots with simulated envelope. The R code and data set are available in the supplementary material.

Suggested Citation

  • Ricardo Rasmussen Petterle & Wagner Hugo Bonat & Cassius Tadeu Scarpin, 2019. "Quasi-beta Longitudinal Regression Model Applied to Water Quality Index Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 346-368, June.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:2:d:10.1007_s13253-019-00360-8
    DOI: 10.1007/s13253-019-00360-8
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    References listed on IDEAS

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