Estate Khmaladze | Searcharxiv

arXiv2016

Unitary transformations, empirical processes and distribution free testing

The main message in this paper is that there are surprisingly many different Brownian bridges, some of them - familiar, some of them - less familiar. Many of these Brownian bridges are very close to Brownian motions. Somewhat loosely speaking, we show that all the bridges can be conveniently mapped onto each other, and hence, to one "standard" bridge. The paper shows that, a consequence of this, we obtain a unified theory of distribution free testing in $\mathbb {R}^d$, both for discrete and continuous cases, and for simple and parametric hypothesis.

math.ST↗

arXiv2014

Note on distribution free testing for discrete distributions

The paper proposes one-to-one transformation of the vector of components $\{Y_{in}\}_{i=1}^m$ of Pearson's chi-square statistic, \[Y_{in}=\frac{ν_{in}-np_i}{\sqrt{np_i}},\qquad i=1,\ldots,m,\] into another vector $\{Z_{in}\}_{i=1}^m$, which, therefore, contains the same "statistical information," but is asymptotically distribution free. Hence any functional/test statistic based on $\{Z_{in}\}_{i=1}^m$ is also asymptotically distribution free. Natural examples of such test statistics are traditional goodness-of-fit statistics from partial sums $\sum_{I\leq k}Z_{in}$. The supplement shows how the approach works in the problem of independent interest: the goodness-of-fit testing of power-law distribution with the Zipf law and the Karlin-Rouault law as particular alternatives.

math.ST↗