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

The present paper is an attempt to answer this question and we emphasize that our main interest is to construct a goodness-of-fit test.. For that, we need some results on

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

The key of the proof is to exhibit a testing procedure minimax in the inverse setting (7) that may fail in certain situations for the direct testing issue. We deal in this paper with

We establish an upper bound on the separation distance associated with this test, and a matching lower bound on the minimax separation rates of testing under non-interactive privacy

In the case of Sobolev null density f 0 , the fact that our testing procedure attains the minimax rate of testing (upper bound of the testing rate (4)), relies on a very sharp

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

The package vsgoftest presented below performs VS GOF tests for several parametric fami- lies of ME distributions: uniform, normal, log-normal, exponential, Pareto, Laplace,