Ver registro no DEDALUS
Exportar registro bibliográfico

Metrics


Metrics:

Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models (2007)

  • Authors:
  • USP affiliated authors: BOLFARINE, HELENO - IME ; ANDRADE FILHO, MÁRIO DE CASTRO - ICMC
  • USP Schools: IME; ICMC
  • DOI: 10.1007/s10463-006-0069-1
  • Subjects: ESTATÍSTICA APLICADA; REGRESSÃO LINEAR
  • Language: Inglês
  • Source:
  • Acesso online ao documento

    Online accessDOI or search this record in
    Informações sobre o DOI: 10.1007/s10463-006-0069-1 (Fonte: oaDOI API)
    • Este periódico é de assinatura
    • Este artigo NÃO é de acesso aberto
    • Cor do Acesso Aberto: closed
    Informações sobre o Citescore
  • Título: Annals of the Institute of Statistical Mathematics

    ISSN: 0020-3157

    Citescore - 2017: 0.96

    SJR - 2017: 1.495

    SNIP - 2017: 1.509


  • Exemplares físicos disponíveis nas Bibliotecas da USP
    BibliotecaCód. de barrasNúm. de chamada
    ICMC1627240-10PROD-1627240
    How to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      ZAVALA, Arturo A. Z.; BOLFARINE, Heleno; CASTRO, Mário de. Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models. Annals of the Institute of Statistical Mathematics[S.l.], v. 59, p. 515-530, 2007. Disponível em: < http://www.springerlink.com.w10077.dotlib.com.br/content/6v31740124143571/fulltext.pdf > DOI: 10.1007/s10463-006-0069-1.
    • APA

      Zavala, A. A. Z., Bolfarine, H., & Castro, M. de. (2007). Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models. Annals of the Institute of Statistical Mathematics, 59, 515-530. doi:10.1007/s10463-006-0069-1
    • NLM

      Zavala AAZ, Bolfarine H, Castro M de. Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models [Internet]. Annals of the Institute of Statistical Mathematics. 2007 ; 59 515-530.Available from: http://www.springerlink.com.w10077.dotlib.com.br/content/6v31740124143571/fulltext.pdf
    • Vancouver

      Zavala AAZ, Bolfarine H, Castro M de. Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models [Internet]. Annals of the Institute of Statistical Mathematics. 2007 ; 59 515-530.Available from: http://www.springerlink.com.w10077.dotlib.com.br/content/6v31740124143571/fulltext.pdf

    Referências citadas na obra
    Carroll R.J., Ruppert D., Stefanski L.A. (1995). Measurement error in nonlinear models. Chapman Hall, New York
    Chan L.K., Mak T.K. (1985). On the polynomial functional relationship. Journal of the Royal Statistical Society Series B 47, 510–518
    Cheng C.-L., Kukush A.G. (2004). A goodness-of-fit test for a polynomial errors-in-variables model. Ukrainian Mathematical Journal 56, 641–661
    Cheng C.-L., Schneeweiss H. (1998). Polynomial regression with errors in the variables. Journal of the Royal Statistical Society Series B 60, 189–199
    Cheng C.-L., Schneeweiss H. (2002). On the polynomial measurement error model. In: Van Huffel S., Lemmerling P. (eds). Total Least Squares and Errors-in-variables Modeling (Leuven, 2001). Kluwer, Dordrecht, pp. 131–143
    Cheng C.-L., Schneeweiss H., Thamerus M. (2000). A small sample estimator for a polynomial regression with errors in the variables. Journal of the Royal Statistical Society Series B 62, 699–709
    Cheng C.-L., Van Ness J.W. (1999). Statistical regression with measurement error. Arnold, London
    de Castro M., de Castilho M.V., Bolfarine H. (2006). Consistent estimation and testing in comparing analytical bias models. Environmetrics 17, 167–182
    Doornik J.A. (2002). Object-oriented matrix programming using Ox. (3rd ed.). Consultants Press and Oxford, London
    Fuller W.A. (1987). Measurement error models. Wiley, New York
    Galea-Rojas M., de Castilho M.V., Bolfarine H., de Castro M. (2003). Detection of analytical bias. Analyst 128, 1073–1081
    Gimenez P., Bolfarine H. (1997). Corrected score functions in classical error-in-variables and incidental parameter models. Australian Journal of Statistics 39, 325–344
    Gimenez P., Bolfarine H., Colosimo E.A. (2000). Hypotheses testing for error-in-variables models. Annals of the Institute of Statistical Mathematics 52, 698–711
    Kuha J., Temple J. (2003). Covariate measurement error in quadratic regression. International Statistics Review 71, 131–150
    Kukush A., Schneeweiss H., Wolf R. (2005). Relative efficiency of three estimators in a polynomial regression with measurement errors. Journal of Statistical Planning and Inference 127, 179–203
    Lehmann E.L. (1998). Elements of large-sample theory. Springer, Berlin Heidleberg New York
    Moon M.-S., Gunst R.F. (1993). Polynomial measurement error modeling. Computational Statistics Data Analysis 19, 1–21
    Nakamura T. (1990). Corrected score function of errors-in-variables models: methodology and applications to generalized linear models. Biometrika 77, 127–137
    Nguyet A.N.M., van Nederkassel A.M., Tallieu L., Kuttatharmmakul S., Hund E., Hu Y., Smeyers-Verbeke J., Heyden Y.V. (2004). Statistical method comparison: short- and long-column liquid chromatography assays of ketoconazole and formaldehyde in shampoo. Analytica Chimica Acta 516, 87–106
    R Development Core Team (2004). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing
    Ripley B.D., Thompson M. (1987). Regression techniques for the detection of analytical bias. Analyst 112, 377–383
    Riu J., Rius F.X. (1996). Assessing the accuracy of analytical methods using linear regression with errors in both axes. Analytical Chemistry 68, 1851–1857
    Schneeweiss H., Nittner T. (2001). Estimating a polynomial regression with measurement errors in the structural and in the functional case – A comparison. In: Saleh A.K.M.E. (eds). Data analysis from statistical foundations. Nova Science, Huntington, pp. 195–205
    Stefanski L.A. (1989). Unbiased estimation of a nonlinear function of a normal mean with application to measurement error models. Communications in Statistics-Theory and Methods 18, 4335–4358
    Synek V. (2001). Calibration lines passing through the origin with errors in both axes. Accreditation and Quality Assurance 6, 360–367
    Thamerus M. (1998). Different nonlinear regression models with incorrectly observed covariates. In: Galata R., Küchenhoff H. (eds). Econometrics in theory and practice. Physica Verlag, Heidelberg, pp. 31–44
    Walter S.D. (1997). Variation in baseline risk as an explanation of heterogeneity in meta-analysis. Statistics in Medicine 16, 2883–2900