Remuestreo

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En el ámbito de la estadística, se denomina remuestreo (en jerga en inglés resampling) a una variedad de métodos que permiten realizar algunas de las siguientes operaciones:

  1. Estimar la precisión de muestras estadísticas (medianas, variancias, percentiles) mediante el uso de subconjuntos de datos disponibles (jackknifing) o tomando datos en forma aleatoria de un conjunto de datos bootstrapping)
  2. Intercambiar marcadores de puntos de datos al realizar tests de significancia (test de permutación, también denominados tests exactos, tests de aleatoriedad, o pruebas de re-aleatoriedad)
  3. Validar modelos para el uso de subconjuntos aleatorios (bootstrapping, validación cruzada)

Entre las técnicas comunes de remuestreo se encuentran bootstrapping, jackknifing y pruebas de permutación.


Bibliografía[editar]

Introducción a la estadística[editar]

  • Good, P. (2005) Introduction to Statistics Through Resampling Methods and R/S-PLUS. Wiley. ISBN 0-471-71575-1
  • Good, P. (2005) Introduction to Statistics Through Resampling Methods and Microsoft Office Excel. Wiley. ISBN 0-471-73191-9
  • Hesterberg, T. C., D. S. Moore, S. Monaghan, A. Clipson, and R. Epstein (2005). Bootstrap Methods and Permutation Tests.
  • Wolter, K.M. (2007). Introduction to Variance Estimation. Second Edition. Springer, Inc.

Técnica de bootstrap[editar]


Técnica de Jackknife[editar]

  • Berger, Y.G. (2007). A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika. Vol. 94, 4, pp. 953-964.
  • Berger, Y.G. and Rao, J.N.K. (2006). Adjusted jackknife for imputation under unequal probability sampling without replacement. Journal of the Royal Statistical Society B. Vol. 68, 3, pp. 531-547.
  • Berger, Y.G. and Skinner, C.J. (2005). A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B. Vol. 67, 1, pp. 79-89.
  • Jiang, J., Lahiri, P. and Wan, S-M. (2002). A unified jackknife theory for empirical best prediction with M-estimation. The Annals of Statistics. Vol. 30, 6, pp. 1782-810.
  • Jones, H.L. (1974). Jackknife estimation of functions of stratum means. Biometrika. Vol. 61, 2, pp. 343-348.
  • Kish, L. and Frankel M.R. (1974). Inference from complex samples. Journal of the Royal Statistical Society B. Vol. 36, 1, pp. 1-37.
  • Krewski, D. and Rao, J.N.K. (1981). Inference from stratified samples: properties of the linearization, jackknife and balanced repeated replication methods. The Annals of Statistics. Vol. 9, 5, pp. 1010-1019.
  • Quenouille, M.H. (1956). Notes on bias in estimation. Biometrika. Vol. 43, pp. 353-360.
  • Rao, J.N.K. and Shao, J. (1992). Jackknife variance estimation with survey data under hot deck imputation. Biometrika. Vol. 79, 4, pp. 811-822.
  • Rao, J.N.K., Wu, C.F.J. and Yue, K. (1992). Some recent work on resampling methods for complex surveys. Survey Methodology. Vol. 18, 2, pp. 209-217.
  • Shao, J. and Tu, D. (1995). The Jackknife and Bootstrap. Springer-Verlag, Inc.
  • Tukey, J.W. (1958). Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics. Vol. 29, 2, pp. 614.
  • Wu, C.F.J. (1986). Jackknife, Bootstrap and other resampling methods in regression analysis. The Annals of Statistics. Vol. 14, 4, pp. 1261-1295.

Métodos Monte Carlo[editar]

  • George S. Fishman (1995). Monte Carlo: Concepts, Algorithms, and Applications, Springer, New York. ISBN 0-387-94527-X.
  • James E. Gentle (2009). Computational Statistics, Springer, New York. Part III: Methods of Computational Statistics. ISBN 978-0-387-98143-7.
  • Christian P. Robert and George Casella (2004). Monte Carlo Statistical Methods, Second ed., Springer, New York. ISBN 0-387-21239-6.
  • Shlomo Sawilowsky and Gail Fahoome (2003). Statistics via Monte Carlo Simulation with Fortran. Rochester Hills, MI: JMASM. ISBN 0-9740236-0-4.


Test de Permutación[editar]

Referencias originales:

  • R. A. Fisher, The Design of Experiment, New York: Hafner, 1935.
  • Pitman, E. J. G., "Significance tests which may be applied to samples from any population", Royal Statistical Society Supplement, 1937; 4: 119-130 and 225-32 (parts I and II).
  • Pitman, E. J. G., "Significance tests which may be applied to samples from any population. Part III. The analysis of variance test", Biometrika, 1938; 29: 322-335.

Referencias modernas:

Métodos computacionales:

  • Mehta, C. R. and Patel, N. R. (1983). 2A network algorithm for performing Fisher’s exact test in r x c contingency tables", J. Amer. Statist. Assoc, 78(382):427–434.
  • Metha, C. R., Patel, N. R. and Senchaudhuri, P. (1988). "Importance sampling for estimating exact probabilities in permutational inference", J. Am. Statist. Assoc., 83(404):999–1005.

Métodos de remuestreo[editar]

  • Good, P. (2006) Resampling Methods. 3rd Ed. Birkhauser.
  • Wolter, K.M. (2007). Introduction to Variance Estimation. 2nd Edition. Springer, Inc.