Araştırma Makalesi
BibTex RIS Kaynak Göster

A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations

Yıl 2013, Cilt: 6 Sayı: 1, 10 - 26, 01.01.2013

Öz

We investigate the estimation of the integral of the square of a multidimensional unknown function f under mild assumptions on the model allowing dependence on the observations. We develop an adaptive estimator based on a plug-in approach and wavelet projections. Taking the mean absolute error and assuming that f has a certain degree of smoothness, we prove that our estimator attains a sharp rate of convergence. Applications are given for the biased density model, the nonparametric regression model and a GARCH-type model under some mixing dependence conditions (alpha-mixing or beta -mixing). A simulation study considering nonparametric regression models with dependent observations illustrates the usefulness of the proposed estimator.

Kaynakça

  • Barbedor, P. (2006). Analyse en composantes ind´ependantes par ondelettes, th`ese Universit´e Paris VII, http://tel.archives-ouvertes.fr/tel-00119428.
  • Bickel, P. J. and Ritov, Y. (1988). Estimating integrated squared density derivatives: Sharp best order of convergence estimates. Sankhya Serie A, 50, 381-393.
  • Brunel, E., Comte, F. and Guilloux, A. (2009). Nonparametric density estimation in presence of bias and censoring. Test, 18, 1, 166-194.
  • Butucea, C. and Comte, F. (2009). Adaptive estimation of linear functionals in the convolution model and applications, Bernoulli, 15, 1, 69-98.
  • Butucea, C. and Meziani, K. (2011). Quadratic functional estimation in inverse problems, Statistical Methodology, 8, 1, 31-41.
  • Cai, T. and Low, M. (2005). Non-quadratic estimators of a quadratic functional. The Annals of Statistics, 33, 2930–2956.
  • Cai, T. and Low, M. (2006). Optimal adaptive estimation of a quadratic functional. The Annals of Statistics, 34, 2298–2325.
  • Carrasco, M. and Chen, X. (2002). Mixing and moment properties of various GARCH and stochastic volatility models. Econometric Theory, 18, 17-39.
  • Chesneau, C. (2011). Adaptive wavelet estimation of a biased density for strongly mixing sequences, International Journal of Mathematics and Mathematical Sciences, Volume 2011, Article ID 604150, 21 pages.
  • Chesneau, C. and Doosti, H. (2012). Wavelet linear density estimation for a GARCH model under various dependence structures, Journal of the Iranian Statistical Society, 11, 1, 1-21.
  • Cohen, A., Daubechies, I., Jawerth, B. and Vial, P. (1993). Wavelets on the interval and fast wavelet transforms. Applied and Computational Harmonic Analysis, 24, 1, 54–81.
  • Cox, D. (1969). Some sampling problems in technology. In New Developments in Survey Sampling (N. L. Johnson and H. Smith, Jr., eds.). Wiley, New York, 506-527.
  • Davydov, Y. (1970). The invariance principle for stationary processes. Theor. Probab. Appl., 15, 3, 498-509.
  • Delaigle, A. and Gijbels, I. (2002). Estimation of integrated squared density derivatives from a contaminated sample, Journal of the Royal Statistical Society, B, 64, 869-886.
  • DeVore, R. and Popov, V. (1988). Interpolation of Besov spaces. Trans. Amer. Math. Soc., 305, 397-414.
  • Donoho, D. L. and Nussbaum, M. (1990). Minimax quadratic estimation of a quadratic functional. Journal of Complexity, 6, 290-323.
  • Doukhan, P. (1994). Mixing. Properties and Examples. Lecture Notes in Statistics 85. Springer Verlag, New York.
  • Efromovich, S. (2004). Density estimation for biased data. Annals of Statistics, 32, (3), 1137-1161.
  • Fryzlewicz, P. and Subba Rao, S. (2011).Mixing properties of ARCH and time-varying ARCH processes, Bernoulli, Volume 17, Number 1 (2011), 320-346.
  • Gayraud, G. and Tribouley, K. (1999). Wavelet methods to estimate an integrated quadratic functional: Adaptivity and asymptotic law. Statistics and Probability Letters, 44, 109-122.
  • Gin´e, E. and Nickl, R. (2008). A Simple Adaptive Estimator of the Integrated Square of a Density. Bernoulli, 14, 47-61.
  • H¨ardle, W. (1990). Applied Nonparametric Regression, Cambridge University Press.
  • H¨ardle, W., Kerkyacharian, G., Picard, D. and Tsybakov, A. (1998). Wavelet, Approximation and Statistical Applications, Lectures Notes in Statistics New York 129, Springer Verlag.
  • Hosseinioun, N., Doosti, H. and Niroumand, H.A. (2009). Wavelet-based estimators of the integrated squared density derivatives for mixing sequences. Pakistan Journal of Statistics, 25(3), 341-350.
  • Johnstone, I. M. (2001a). Chi-square oracle inequalities. In State of the art in probability and statistics (Leiden, 1999), Lecture Notes - Monograph Series, volume 36, 399-418. Institute of Mathematical Statistics, Beachwood, OH.
  • Johnstone, I. M. (2001b). Thresholding for weighted chi-square. Statistica Sinica, 11, 691-704.
  • Kerkyacharian, G. and Picard, D. (1996). Estimating nonquadratic functionals of a density using haar wavelets. The Annals of Statistics, 24 (2), 485-507.
  • Laurent, B. (2005). Adaptive estimation of a quadratic functional of a density by model selection. ESAIM PS, 9, 1-18.
  • Liang, H. (2011). Asymptotic normality of wavelet estimator in heteroscedastic model with α-mixing error. J Syst Sci Complex, 24, (4), 725-737.
  • Mallat, S. (2009). A wavelet tour of signal processing. Elsevier/ Academic Press, Amsterdam, third edition. The sparse way, With contributions from Gabriel Peyr´e.
  • Marron, J.S., Adak, S., Johnstone, I.M., Neumann, M.H. and Patil, P. (1998). Exact risk analysis of wavelet regression. J. Comput. Graph. Statist, 7, 278-309.
  • Meyer, Y. (1992). Wavelets and Operators. Cambridge University Press, Cambridge.
  • Petsa, A. and Sapatinas, T. (2010). Adaptive quadratic functional estimation of a weighted density by model selection. Statistics, 44, 571-585.
  • Prakasa Rao. B.L.S. (1999). Estimation of the integrated squared density derivatives by wavelets. Bull. Inform. Cyb., 31, 47-65.
  • Rosenblatt, M. (1956). A central limit theorem and a strong mixing condition. Proc. Nat. Ac. Sc. USA, 42, 43-47.
  • Shao, Q.-M. (1995). Maximal inequality for partial sums of ρ-mixing sequences. Ann. Probab., 23, 948- 965.
  • Tsybakov, A.B. (2004). Introduction `a l’estimation non-param´etrique, Springer.
  • Vidakovic, B. (1999). Statistical Modeling by Wavelets. John Wiley & Sons, Inc., New York, 384 pp.
  • Viennet, G. (1997). Inequalities for absolutely regular processes: application to density estimation. Probab. Theory Related Fields, 107, 467-492.
  • White, H. and Domowitz, I. (1984). Nonlinear Regression with Dependent Observations. Econometrica, 52, 143-162.
Yıl 2013, Cilt: 6 Sayı: 1, 10 - 26, 01.01.2013

Öz

Kaynakça

  • Barbedor, P. (2006). Analyse en composantes ind´ependantes par ondelettes, th`ese Universit´e Paris VII, http://tel.archives-ouvertes.fr/tel-00119428.
  • Bickel, P. J. and Ritov, Y. (1988). Estimating integrated squared density derivatives: Sharp best order of convergence estimates. Sankhya Serie A, 50, 381-393.
  • Brunel, E., Comte, F. and Guilloux, A. (2009). Nonparametric density estimation in presence of bias and censoring. Test, 18, 1, 166-194.
  • Butucea, C. and Comte, F. (2009). Adaptive estimation of linear functionals in the convolution model and applications, Bernoulli, 15, 1, 69-98.
  • Butucea, C. and Meziani, K. (2011). Quadratic functional estimation in inverse problems, Statistical Methodology, 8, 1, 31-41.
  • Cai, T. and Low, M. (2005). Non-quadratic estimators of a quadratic functional. The Annals of Statistics, 33, 2930–2956.
  • Cai, T. and Low, M. (2006). Optimal adaptive estimation of a quadratic functional. The Annals of Statistics, 34, 2298–2325.
  • Carrasco, M. and Chen, X. (2002). Mixing and moment properties of various GARCH and stochastic volatility models. Econometric Theory, 18, 17-39.
  • Chesneau, C. (2011). Adaptive wavelet estimation of a biased density for strongly mixing sequences, International Journal of Mathematics and Mathematical Sciences, Volume 2011, Article ID 604150, 21 pages.
  • Chesneau, C. and Doosti, H. (2012). Wavelet linear density estimation for a GARCH model under various dependence structures, Journal of the Iranian Statistical Society, 11, 1, 1-21.
  • Cohen, A., Daubechies, I., Jawerth, B. and Vial, P. (1993). Wavelets on the interval and fast wavelet transforms. Applied and Computational Harmonic Analysis, 24, 1, 54–81.
  • Cox, D. (1969). Some sampling problems in technology. In New Developments in Survey Sampling (N. L. Johnson and H. Smith, Jr., eds.). Wiley, New York, 506-527.
  • Davydov, Y. (1970). The invariance principle for stationary processes. Theor. Probab. Appl., 15, 3, 498-509.
  • Delaigle, A. and Gijbels, I. (2002). Estimation of integrated squared density derivatives from a contaminated sample, Journal of the Royal Statistical Society, B, 64, 869-886.
  • DeVore, R. and Popov, V. (1988). Interpolation of Besov spaces. Trans. Amer. Math. Soc., 305, 397-414.
  • Donoho, D. L. and Nussbaum, M. (1990). Minimax quadratic estimation of a quadratic functional. Journal of Complexity, 6, 290-323.
  • Doukhan, P. (1994). Mixing. Properties and Examples. Lecture Notes in Statistics 85. Springer Verlag, New York.
  • Efromovich, S. (2004). Density estimation for biased data. Annals of Statistics, 32, (3), 1137-1161.
  • Fryzlewicz, P. and Subba Rao, S. (2011).Mixing properties of ARCH and time-varying ARCH processes, Bernoulli, Volume 17, Number 1 (2011), 320-346.
  • Gayraud, G. and Tribouley, K. (1999). Wavelet methods to estimate an integrated quadratic functional: Adaptivity and asymptotic law. Statistics and Probability Letters, 44, 109-122.
  • Gin´e, E. and Nickl, R. (2008). A Simple Adaptive Estimator of the Integrated Square of a Density. Bernoulli, 14, 47-61.
  • H¨ardle, W. (1990). Applied Nonparametric Regression, Cambridge University Press.
  • H¨ardle, W., Kerkyacharian, G., Picard, D. and Tsybakov, A. (1998). Wavelet, Approximation and Statistical Applications, Lectures Notes in Statistics New York 129, Springer Verlag.
  • Hosseinioun, N., Doosti, H. and Niroumand, H.A. (2009). Wavelet-based estimators of the integrated squared density derivatives for mixing sequences. Pakistan Journal of Statistics, 25(3), 341-350.
  • Johnstone, I. M. (2001a). Chi-square oracle inequalities. In State of the art in probability and statistics (Leiden, 1999), Lecture Notes - Monograph Series, volume 36, 399-418. Institute of Mathematical Statistics, Beachwood, OH.
  • Johnstone, I. M. (2001b). Thresholding for weighted chi-square. Statistica Sinica, 11, 691-704.
  • Kerkyacharian, G. and Picard, D. (1996). Estimating nonquadratic functionals of a density using haar wavelets. The Annals of Statistics, 24 (2), 485-507.
  • Laurent, B. (2005). Adaptive estimation of a quadratic functional of a density by model selection. ESAIM PS, 9, 1-18.
  • Liang, H. (2011). Asymptotic normality of wavelet estimator in heteroscedastic model with α-mixing error. J Syst Sci Complex, 24, (4), 725-737.
  • Mallat, S. (2009). A wavelet tour of signal processing. Elsevier/ Academic Press, Amsterdam, third edition. The sparse way, With contributions from Gabriel Peyr´e.
  • Marron, J.S., Adak, S., Johnstone, I.M., Neumann, M.H. and Patil, P. (1998). Exact risk analysis of wavelet regression. J. Comput. Graph. Statist, 7, 278-309.
  • Meyer, Y. (1992). Wavelets and Operators. Cambridge University Press, Cambridge.
  • Petsa, A. and Sapatinas, T. (2010). Adaptive quadratic functional estimation of a weighted density by model selection. Statistics, 44, 571-585.
  • Prakasa Rao. B.L.S. (1999). Estimation of the integrated squared density derivatives by wavelets. Bull. Inform. Cyb., 31, 47-65.
  • Rosenblatt, M. (1956). A central limit theorem and a strong mixing condition. Proc. Nat. Ac. Sc. USA, 42, 43-47.
  • Shao, Q.-M. (1995). Maximal inequality for partial sums of ρ-mixing sequences. Ann. Probab., 23, 948- 965.
  • Tsybakov, A.B. (2004). Introduction `a l’estimation non-param´etrique, Springer.
  • Vidakovic, B. (1999). Statistical Modeling by Wavelets. John Wiley & Sons, Inc., New York, 384 pp.
  • Viennet, G. (1997). Inequalities for absolutely regular processes: application to density estimation. Probab. Theory Related Fields, 107, 467-492.
  • White, H. and Domowitz, I. (1984). Nonlinear Regression with Dependent Observations. Econometrica, 52, 143-162.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Christophe Chesneau

Maher Kachour Bu kişi benim

Fabien Navarro Bu kişi benim

Yayımlanma Tarihi 1 Ocak 2013
Kabul Tarihi 9 Ekim 2012
Yayımlandığı Sayı Yıl 2013 Cilt: 6 Sayı: 1

Kaynak Göster

APA Chesneau, C., Kachour, M., & Navarro, F. (2013). A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations. Istatistik Journal of The Turkish Statistical Association, 6(1), 10-26.
AMA Chesneau C, Kachour M, Navarro F. A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations. IJTSA. Ocak 2013;6(1):10-26.
Chicago Chesneau, Christophe, Maher Kachour, ve Fabien Navarro. “A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations”. Istatistik Journal of The Turkish Statistical Association 6, sy. 1 (Ocak 2013): 10-26.
EndNote Chesneau C, Kachour M, Navarro F (01 Ocak 2013) A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations. Istatistik Journal of The Turkish Statistical Association 6 1 10–26.
IEEE C. Chesneau, M. Kachour, ve F. Navarro, “A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations”, IJTSA, c. 6, sy. 1, ss. 10–26, 2013.
ISNAD Chesneau, Christophe vd. “A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations”. Istatistik Journal of The Turkish Statistical Association 6/1 (Ocak 2013), 10-26.
JAMA Chesneau C, Kachour M, Navarro F. A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations. IJTSA. 2013;6:10–26.
MLA Chesneau, Christophe vd. “A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations”. Istatistik Journal of The Turkish Statistical Association, c. 6, sy. 1, 2013, ss. 10-26.
Vancouver Chesneau C, Kachour M, Navarro F. A Note On The Adaptive Estimation Of A Quadratic Functional From Dependent Observations. IJTSA. 2013;6(1):10-26.