Detection and Estimation of Jump Points in Non parametric Regression Function withAR(1) Noise

Autor:	Dan Wang, Pengjiang Guo
Rok vydání:	2014
Předmět:	Statistics and Probability Noise Wavelet Autoregressive model Rate of convergence Consistency (statistics) Statistics Estimator Applied mathematics Mathematics Nonparametric regression Statistical hypothesis testing
Zdroj:	Communications in Statistics - Theory and Methods. 44:1097-1110
ISSN:	1532-415X 0361-0926
DOI:	10.1080/03610926.2013.851242
Popis:	In this paper, we propose a method based on wavelet analysis to detect and estimate jump points in non parametric regression function. This method is applied to AR(1) noise process under random design. First, the test statistics are constructed on the empirical wavelet coefficients. Then, under the null hypothesis, the critical values of test statistics are obtained. Under the alternative, the consistency of the test is proved. Afterward, the rate of convergence, the estimators of the number, and locations of change points are given theoretically. Finally, the excellent performance of our method is demonstrated through simulations using artificial and real datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::84d92ac5a23799496684e2c6836b4e68 https://doi.org/10.1080/03610926.2013.851242 Zobrazit plný text záznamu