Published
Wavelet Shrinkage Generalized Bayes Estimation for Multivariate Normal Distribution Mean Vectors with unknown Covariance Matrix under Balanced-LINEX Loss
Contracción de la ondícula Estimación de Bayes generalizada para vectores medios de distribución normal multivariante con matriz de covarianza desconocida con pérdida de LINEX equilibrada
DOI:
https://doi.org/10.15446/rce.v45n1.92037Keywords:
Admissibility, Generalized Bayes estimator, Balanced-LINEX loss, Minimaxity, Multivariate normal distribution, Soft wavelet shrinkage estimator (en)Admisibilidad, Estimador de Bayes generalizado, Estimador de contracción de ondas suaves, Distribución normal multivariante, Minimaxidad, Pérdida de LINEX equilibrada (es)
Downloads
In this paper, the generalized Bayes estimator of mean vector parameter for multivariate normal distribution with Unknown mean vector and covariance matrix is considered. This estimation is performed under the balanced-LINEX error loss function. The generalized Bayes estimator by using wavelet transformation is investigated. We also prove admissibility and minimaxity of shrinkage estimator and we present the simulation study and real data set for test validity of new estimator.
En este trabajo, se considera el estimador de Bayes generalizado del parámetro de vector medio para distribución normal multivariante con vector de media desconocido y matriz de covarianza. Esta estimación se realiza bajo la función de pérdida de error LINEX balanceada. Se investiga el estimador de Bayes generalizado mediante la transformación de ondículas. También probamos la admisibilidad y minimaxidad del estimador de contracción y presentamos el estudio de simulación y el conjunto de datos reales para
comprobar la validez de la prueba del nuevo estimador.
References
Cao, M. X. & He, D. (2017), ‘Admissibility of linear estimators of the common mean parameter in general linear models under a balanced loss function’, Journal of Multivariate Analysis 153, 246–254. DOI: https://doi.org/10.1016/j.jmva.2016.10.003
Donoho, D. L. & Johnstone, I. M. (1994), ‘Ideal spatial adaptation by wavelet shrinkage’, Biometrika 81, 425–455. DOI: https://doi.org/10.1093/biomet/81.3.425
Fourdrinier, D. & Strawderman, W. E. (2015), ‘Robust minimax stein estimation under invariant data-based loss for spherically and elliptically symmetric distributions’, Metrika 78(4), 461–484. DOI: https://doi.org/10.1007/s00184-014-0512-x
Guo, C., Ma, Y., Yang, B., S., J. C. & Kaul, M. (2012), ‘EcoMark: Evaluating models of vehicular environmental impact’, SIGSPATIAL/GIS pp. 269–278. DOI: https://doi.org/10.1145/2424321.2424356
Huang, S. Y. (2002), ‘On a Bayesian aspect for soft wavelet shrinkage estimation under an asymmetric linex loss’, Statistics and Probability Letters 56, 171–175.
Jiang, W. & Zhang, C. H. (2009), ‘General maximum likelihood empirical Bayes estimation of normal means’, The Annals of Statistics 37(4), 1647–1684.
Joly, E. Lugosi, G. & Oliveira, R. I. (2017), ‘On the estimation of the mean of a random vector’, Electronic Journal of Statistics 11, 440–451.
Jozani, J. M., Leblanc, A. & Marchand, E. (2014), ‘On continuous distribution functions, minimax and best invariant estimators, and integrated balanced loss functions’, Canadian Journal of Statistics 42, 470–486.
Jozani, M. J., Marchand, É. & Parsian, A. (2006), ‘On estimation with weighted balanced-type loss function’, Statistics and Probability Letters 76, 733–780.
Jozani, M. J., Marchand, É. & Parsian, A. (2012), ‘Bayesian and Robust Bayesian analysis under a general class of balanced loss functions’, Statistical Papers 53, 51–60.
Karamikabir, H. & Afshari, M. (2019), ‘Wavelet Shrinkage Generalized Bayes Estimation for Elliptical Distribution Parameters under LINEX Loss’, International Journal of Wavelets, Multiresolution and Information Processing 14(1), 1950009.
Karamikabir, H. & Afshari, M. (2020), ‘Generalized Bayesian Shrinkage and Wavelet Estimation of Location Parameter for Spherical Distribution under Balance-type Loss: Minimaxity and Admissibility,’, Journal of Multivariate Analysis 177(1), 104583.
Karamikabir, H. & Afshari, M. (2021), ‘New wavelet SURE thresholds of elliptical distributions under the balance loss’, Statistica Sinica 31(4), 1829–1852.
Karamikabir, H. Afshari, M. & Arashi, M. (2018), ‘Shrinkage estimation of nonnegative
mean vector with unknown covariance under balance loss’, Journal of Inequalities and Applications 2018, 331.
Karamikabir, H., Afshari, M. & Lak, F. (2020), ‘Wavelet threshold based on Stein’s unbiased risk estimators of restricted location parameter in multivariate normal’, Journal of Applied Statistics 48(10), 1712–1729.
Marchand, E. & Strawderman, W. E. (2020), ‘On shrinkage estimation for balanced loss functions’, Journal of Multivariate Analysis 175, 104558.
Pal, N., Sinha, B. K., Chaudhuri, G. & Chang, C. H. (2007), ‘Estimation Of A Multivariate Normal Mean Vector And Local Improvements’, Statistics 26(1), 1– 7.
Rencher, A. C. & Christensen, W. F. (2012), Methods of Multivariate Analysis, third edition edn, John Wiley & Sons.
Rudin, W. (1976), Principle of Mathematical Analysis, MacGraw-Hill.
Huang, S. Y. (2002), ‘On a Bayesian aspect for soft wavelet shrinkage estimation under an asymmetric linex loss’, Statistics and Probability Letters 56, 171–175. DOI: https://doi.org/10.1016/S0167-7152(01)00181-X
Jiang, W. & Zhang, C. H. (2009), ‘General maximum likelihood empirical Bayes estimation of normal means’, The Annals of Statistics 37(4), 1647–1684. DOI: https://doi.org/10.1214/08-AOS638
Joly, E. Lugosi, G. & Oliveira, R. I. (2017), ‘On the estimation of the mean of a random vector’, Electronic Journal of Statistics 11, 440–451. DOI: https://doi.org/10.1214/17-EJS1228
Jozani, J. M., Leblanc, A. & Marchand, E. (2014), ‘On continuous distribution functions, minimax and best invariant estimators, and integrated balanced loss functions’, Canadian Journal of Statistics 42, 470–486. DOI: https://doi.org/10.1002/cjs.11217
Jozani, M. J., Marchand, É. & Parsian, A. (2006), ‘On estimation with weighted balanced-type loss function’, Statistics and Probability Letters 76, 733–780. DOI: https://doi.org/10.1016/j.spl.2005.10.026
Jozani, M. J., Marchand, É. & Parsian, A. (2012), ‘Bayesian and Robust Bayesian analysis under a general class of balanced loss functions’, Statistical Papers 53, 51–60. DOI: https://doi.org/10.1007/s00362-010-0307-8
Karamikabir, H. & Afshari, M. (2019), ‘Wavelet Shrinkage Generalized Bayes Estimation for Elliptical Distribution Parameters under LINEX Loss’, International Journal of Wavelets, Multiresolution and Information Processing 14(1), 1950009. DOI: https://doi.org/10.1142/S0219691319500097
Karamikabir, H. & Afshari, M. (2020), ‘Generalized Bayesian Shrinkage and Wavelet Estimation of Location Parameter for Spherical Distribution under Balance-type Loss: Minimaxity and Admissibility,’, Journal of Multivariate Analysis 177(1), 104583. DOI: https://doi.org/10.1016/j.jmva.2019.104583
Karamikabir, H. & Afshari, M. (2021), ‘New wavelet SURE thresholds of elliptical distributions under the balance loss’, Statistica Sinica 31(4), 1829–1852. DOI: https://doi.org/10.5705/ss.202019.0339
Karamikabir, H. Afshari, M. & Arashi, M. (2018), ‘Shrinkage estimation of nonnegative mean vector with unknown covariance under balance loss’, Journal of Inequalities and Applications 2018, 331. DOI: https://doi.org/10.1186/s13660-018-1919-0
Karamikabir, H., Afshari, M. & Lak, F. (2020), ‘Wavelet threshold based on Stein’s unbiased risk estimators of restricted location parameter in multivariate normal’, Journal of Applied Statistics 48(10), 1712–1729. DOI: https://doi.org/10.1080/02664763.2020.1772209
Marchand, E. & Strawderman, W. E. (2020), ‘On shrinkage estimation for balanced loss functions’, Journal of Multivariate Analysis 175, 104558. DOI: https://doi.org/10.1016/j.jmva.2019.104558
Pal, N., Sinha, B. K., Chaudhuri, G. & Chang, C. H. (2007), ‘Estimation of A Multivariate Normal Mean Vector And Local Improvements’, Statistics 26(1), 1–17. DOI: https://doi.org/10.1080/02331889508802463
Rencher, A. C. & Christensen, W. F. (2012), Methods of Multivariate Analysis, third edition edn, John Wiley & Sons. DOI: https://doi.org/10.1002/9781118391686
Rudin, W. (1976), Principle of Mathematical Analysis, MacGraw-Hill.
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).