EFFICIENCY OF A KERNEL DENSITY ESTIMATOR UNDER AN AUTOREGRESSIVE DEPENDENCE MODEL Academic Article

abstract

• The problem of estimating the probability density function of a strictly stationary process is considered. To study the effect of a dependence structure on the efficiency of a kernel density estimator, the mean integrated squared error (MISE) of the Fourier integral estimator (FIE) is derived on the assumption that the observed data are generated by a first-order autoregressive process. Numerical results for the normal and Cauchy densities show that even moderate departures from independence can lead to a considerable loss in efficiency of the FIE. In addition to efficiency considerations, the issue of determining an optimal smoothing parameter for the FIE under the autoregressive model is addressed. 1984 Taylor & Francis Group, LLC.

authors

• Hart, Jeffrey

published proceedings

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

author list (cited authors)

• HART, J. D.

citation count

• 25

complete list of authors

• HART, JD

publication date

• March 1984

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION  Journal

keywords

• 49 Mathematical Sciences
• 4905 Statistics

Digital Object Identifier (DOI)

• 10.2307/2288344

start page

• 110

end page

• 117

volume

• 79

issue

• 385

URL

• http%3A%2F%2Fdx.doi.org%2F10.1080%2F01621459.1984.10477072