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Meny g艂贸wne

Autor
Bouabsa Wahiba (University Djillali LIABES of Sidi Bel Abbes, Algeria)
Tytu艂
The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random
Szacowanie g臋sto艣ci warunkowej z wykorzystaniem modelu w strukturze regresji skalarnej na funkcji: lokalne podej艣cie liniowe z losowym brakiem
殴r贸d艂o
Econometrics. Advances in Applied Data Analysis, 2023, vol. 27, nr 1, s. 17-32, rys., tab., bibliogr. 27 poz.
Ekonometria
S艂owa kluczowe
Ekonometria, Estymacja, Statystyka
Econometrics, Estimation, Statistics
Uwagi
Klasyfikacja JEL: C13, C14, C15
streszcz., summ.
Abstrakt
Celem analizy by艂o zbadanie nieparametrycznego estymatora funkcji g臋sto艣ci i trybu skalarnej zmiennej odpowiedzi na zmienn膮 funkcyjn膮, gdy obserwacje s膮 i.i.d. Ten proponowany estymator jest tworzony przez po艂膮czenie metody Missing At Random (MAR) z lokalnym podej艣ciem liniowym. Na koniec zapewniono r贸wnie偶 badanie por贸wnawcze oparte na symulowanych danych, aby zilustrowa膰 wydajno艣膰 sko艅czonej pr贸bki i przydatno艣膰 lokalnego podej艣cia liniowego z MAR do obecno艣ci nawet niewielkiej cz臋艣ci warto艣ci odstaj膮cych w danych.(abstrakt oryginalny)

The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.(original abstract)
Dost臋pne w
Biblioteka SGH im. Profesora Andrzeja Grodka
Pe艂ny tekst
Poka偶
Bibliografia
Poka偶
  1. Almanjahie, I., Kaid, Z., Laksaci, A., & Rachdi, M. (2022). Estimating the conditional density in scalar- -on-function regression structure: k-N-N local linear approach.Mathematics, 10(6), 1-16.
  2. Attouch, M.., Bouabsa, W., & Chiker el Mozoaur, Z. (2018). The 饾憳饾憳-nearest neighbors estimation of the conditional mode for functional data under dependency. International Journal of Statistics & Economics, 19(1), 48-60.
  3. Attouch, M., & Bouabsa, W. (2013). The 饾憳饾憳-nearest neighbors estimation of the conditional mode for functional data. Rev. Roumaine Math. Pures Appl., 58(4), 393-415.
  4. Attouch, M., Laksaci, A., & Messabihi, N. (2015). Nonparametric relative error regression for spatial random variables. Statistical Papers, 58(4), 987-1008.
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  6. Ba矛llo, A. & Gran茅, A. (2009). Local linear regression for functional predictor and scalar response. Journal of Multivariate Analysis, 100, 102-111.
  7. Barrientos-Marin, J., Ferraty, F., & Vieu, P. (2010). Locally modelled regression and functional data. Journal of Nonparametric Statistics, 22(5), 617-632.
  8. Boj, E., Delicado, P., & Fortiana, J. (2010). Distance-based local linear regression for functional predictors, Computational Statistics and Data Analysis, 54, 429-437.
  9. Chaouch, M., La饾憱饾憱b, N., & Louani, D. (2014). Rate of uniform consistency for a class of mode regression on functional stationary ergodic data. Stat. Meth. Appl., 26, 19-47.
  10. Chahad, A., A茂t-Hennani, L., & Laksaci, L. (2017). Functional local linear estimate for functional relative-error regression. Journal of Statistical Theory and Practice, 13(11), 771-789.
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  13. Demongeot, J., Laksaci, A., Madani, F., & Rachdi, M. (2013). Functional data: local linear estimation of the conditional density and its application. Statistics, 47, 26-44.
  14. Ezzahrioui, M., & Ould-Said, E. (2006). On the asymptotic properties of a nonparametric estimator of the conditional mode for functional dependent data. Preprint, LMPA No 277, Univ. du Littoral C么te d'Opale.
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  17. Ferraty, F., Rabhi, A., & Vieu, P. (2008). Estimation non-param茅trique de la fonction de hasard avec variable explicative fonctionnelle. Revue de Math茅matiques Pures et Appliqu茅es, 53, 1-18.
  18. Ferraty, F., & Vieu, P. (2006). Nonparametric functional data analysis. Theory and practice. Springer- -Verlag.
  19. Ferraty, F. (2010). High-dimensional data: a fascinating statistical challenge. J. Multivariariate Anal., 101, 305-306.
  20. Giraldo, R. Dabo-Niang S., & Martinez, S. (2018). Statistical modeling of spatial big data: an approach from a functional data analysis perspective. Statist. Probab. Lett., 136, 126-129.
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  22. Kirkby, J. L., Leitao, A., & Nguyen, D. (2021). Nonparametric density estimation and bandwidth selection with B-spline bases: a novel Galerkin method. Comput. Statist. Data Anal., 159.
  23. Laib, N., & Ouled-Said, E. (2000). A robust nonparametric estimation of the autoregression function under an ergodic hypothesis. Canad. J. Statist., 28, 817-828.
  24. Maillot, B., & Chesneau, C. (2021). On the conditional density estimation for continuous time processes with values functional in spaces. Statist. Probab. Lett., 178.
  25. Rachdi, M., Laksaci, A., Demongeot, J., Abdali, A., & Madani, F. (2014). Theoretical and practical aspects on the quadratic error in the local linear estimation of the conditional density for functional data. Computational Statistics & Data Analysis, 73, 53-68.
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Cytowane przez
Poka偶
ISSN
1507-3866
J臋zyk
eng
URI / DOI
http://dx.doi.org/10.15611/eada.2023.1.02
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