Minimum Distance and Discrepancy Approach for Estimation and Testing Goodness of Fit

Finally, in Section 4, we describe the approach unifying the proof of minimax nonparametric lower bounds from Sections 2 and 3 and prove them for nonparametric setups of Sobolev

This paper has provided sufficient conditions for the consistency of the Bayes factor in testing goodness of fit of a parametric density function and the conditions are verified for

More speci…cally, we are concerned with the correct speci…cation (or model selection) of the dynamic structure with time series and/or spatial stationary processes fx (t)gt 2Z de…ned

as Instead of using a functional basis as in Verdinelli and Wasserman 1998, we represent as the infinite mixture of Beta distributions, Estimation within both parametric

In the present work 1 , we propose a nonparametric testing framework in which we develop a generalized likelihood ratio test to perform curve registration.. We first prove that,

Our goal is to investigate the asymptotic properties of a kernel density estimator asso- ciated with the driven noise of a linear regression in adaptive tracking and to propose

Assuming the smoothness parameter of f to be known, the authors of [9] define a version of the Bickel–Rosenblatt test statistic and study its asymptotic distribution under the

In particular, a one- or two-level parametric bootstrap approach is valid in goodness-of-fit testing for copula models using either the empirical copula process or its