Search Results - subject:"Density estimator"

Nonparametric confidence bands in deconvolution density estimation

Bissantz1, Nicolai; Dümbgen, Lutz; Holzmann, Hajo; … - 2007

asymptotic distribution of the supremum of a centered kernel density estimator. Since then, their method has been further … density estimator for the j th derivative of f,givenby, ˆ f (j) n (x)= ˆ f (j) n,h (x)= 1 2π integraldisplay R (−it) j exp … at minimizing the L ∞ -distance between the density estimator and the unknown true density f. This is in accordance with …

Persistent link: https://www.econbiz.de/10003835964

Nonparametric estimation of the costs of non-sequential search

Moraga-González, José Luis; Sandor, Zsolt; … - 2007

-nonparametric density estimator whose parameters are chosen to maximize the joint likelihood corresponding to all the markets. A Monte Carlo …

Persistent link: https://www.econbiz.de/10011373819

Nonparametric Estimation of Dynamic Panel Models with Fixed Effects

Lee, Yoonseok - 2007

This paper considers nonparametric estimation of autoregressive panel models with fixed effects. A within-group type series estimator is developed and its convergence rate and the asymptotic normality are derived. It is found that the series estimator is asymptotically biased and the bias...

Persistent link: https://www.econbiz.de/10014052263

Semiparametric Regression with Kernel Error Model

Yuan, Ao; Gooijer, Jan G. De - 2006

We propose and study a class of regression models, in which the mean function is specified parametrically as in the existing regression methods, but the residual distribution is modeled nonparametrically by a kernel estimator, without imposing any assumption on its distribution. This...

Persistent link: https://www.econbiz.de/10010325609

Semiparametric Regression with Kernel Error Model

Yuan, Ao; Gooijer, Jan G. De - Tinbergen Instituut - 2006

Persistent link: https://www.econbiz.de/10011257647

Nonparametric Density Estimation for Linear Processes with Infinite Variance

Honda, Toshio - Graduate School of Economics, Hitotsubashi University - 2006

We consider nonparametric estimation of marginal density functions of linear processes by using kernel density estimators. We assume that the innovation processes are i.i.d. and have infinite-variance. We present the asymptotic distributions of the kernel density estimators with the order of...

Persistent link: https://www.econbiz.de/10004992572