Ir al contenido

Documat


Modelo unibimodal simétrico-asimétrico con aplicación al estudio del RNA VIH-1

  • Martínez Flórez, Guillermo [2] ; Moreno Arenas, Germán [1]
    1. [1] Universidad Industrial de Santander

      Universidad Industrial de Santander

      Colombia

    2. [2] Universidad de Córdoba
  • Localización: Integración: Temas de matemáticas, ISSN 0120-419X, Vol. 32, Nº. 1, 2014 (Ejemplar dedicado a: Revista Integración), págs. 1-18
  • Idioma: español
  • Títulos paralelos:
    • Uni-bimodal Symmetric-Asymmetric Model with Application to the Study of HIV-1 RNA
  • Enlaces
  • Resumen
    • español

      Se definen dos nuevas distribuciones de probabilidad: modelo unibimodal simétrico con función de riesgo proporcional a la distribución normal y modelo unibimodal asimétrico con función de riesgo proporcional a la distribución normal asimétrica. Estos modelos permiten ajustar datos censurados con comportamiento bimodal y altos (o bajos) niveles de curtosis comparado con la curtosis de la distribución normal y altos (o bajos) niveles de asimetría. Además, se estiman los parámetros de los modelos por máxima verosimilitud y se determina la matriz de información observada. La flexibilidad de la nueva distribución se ilustra ajustando un conjunto de datos reales: el número de moléculas de ARN VIH-1 por mililitros de sangre medida en personas con pruebas confirmadas de presencia del VIH. 

    • English

      We define two new probability distributions, unibimodal symmetric model with proportional hazard function to the normal distribution and unibimodal asymmetric model with proportional hazard function to the skew normal distribution. These models allow adjust censored data with bimodal behavior and high (or low) levels of kurtosis compared with kurtosis of the normal distribution and high (or low) levels of asymmetry. The model parameters are estimated by maximum likelihood and the observed information matrix is determined. The flexibility of the new distribution is illustrated by adjusting a set of real data, the number of molecules of HIV-1 RNA per milliliter of blood measured in individuals with confirmed test of the presence of HIV.

  • Referencias bibliográficas
    • Citas [1] Amplicor HIV-1 MONITOR R . Test, version 1.5, Branchburg, NJ: Roche Molecular Systems, Inc., 2002.
    • [2] Arnold B., Gómez H. and Salinas H., “On multiple constraint skewed models”, Statistics 43 (2009), no. 3, 279–293.
    • [3] Azzalini A. “A class of distributions which includes the normal ones”, Scandinavian Journal of Statistics 12 (1985), 171–178.
    • [4] Biomarkers Definitions Working Group: Biomarkers and surrogate endpoints. Preferred definitions and conceptual framework. Clin. Pharmacol....
    • [5] Chiogna M., “Some results on the scalar skew-normal distribution”, Journal of the Italian Statistical Society 1 (1998), 1–14.
    • [6] COBAS R AmpliPrep/COBAS R TaqMan R HIV-1 Test, version 2.0. Branchburg, NJ: Roche Molecular Systems, Inc., 2010.
    • [7] Cordeiro G. and de Castro M., “A new family of generalized distributions”, J. Statist. Comput. Simul. 81 (2012), 883–898.
    • [8] Durrans S.R., “Distributions of fractional order statistics in hydrology”, Water Resources Research 28 (1992), no. 6, 1649–1655.
    • [9] Gómez H., Venegas O., and Bolfarine H., “Skew-symmetric distributions generated by the distribution function of the normal distribution”,...
    • [10] Gómez H., Elal-Olivero D., Salinas H., and Bolfarine H., “Bimodal extension based on the Skew-Normal distribution with application to...
    • 62.
    • [11] Hartigan J.A. and Hartigan P.M., “The dip test of unimodality”, Ann. Stat. 13 (1985), 70–84.
    • [12] Hartigan P.M., “Computation of the dip statistics to test for unimodality”, Appl. Stat. 34 (1985), 320–325.
    • [13] Henze N., “A probabilistic representation of the skew-normal distribution”, Scandinavian Journal of Statistics 13 (1986), 271–275.
    • [14] Kim H., “On a class of two-piece skew-normal distribution”, Statistic 39 (2005), no. 6, 537–553.
    • [15] Li X., Chu H., Gallant J., Hoover D., Mack W., Chmiel J., and Muñoz A., “Bimodal virological response to antiretroviral therapy for HIV...
    • [16] Lim T., Bakri R., Morad Z., and Hamid M., “Bimodality in blood glucose distribution. Is it universal?”, Diabetes Care 25 (2002), 2212–2217.
    • [17] O’Hagan A. and Leonard T., “Bayes estimation subject to uncertainty about parameter constraints”, Biometrika 63 (1976), 201–203.
    • [18] Pewsey A., “Problems of inference for Azzalini’s skew-normal distribution”, Journal of Applied Statistics 27 (2000), no. 7, 859–870.
    • [19] R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria,...
    • [20] Stasinopoulos M.D. and Rigby R.A., “Generalized Additive Models for Location Scale and Shape (GAMLSS) in R”, Journal of Statistical Software...
    • [21] Schneider M., Margolick J., Jacobson L., Reddy S., Martinez–Maza O., and Muñoz A.,“Improved estimation of the distribution of suppressed...
    • effective antiretroviral therapy”, Journal Acquir. Immune. Defic. Syndr. 59 (2012), no. 4, 389–392.
    • [22] Tobin J., “Estimation of relationships for limited dependent variables”, Econometrica 26 (1958), no. 1, 24–36.
    • [23] Zhang C., Mapes B.E., and Soden B.J., “Bimodality in tropical water vapour”, Q. J. R. Meteorol. Soc. 129 (2003), 2847–2866.

Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno