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Computer Science, Data Structures and Algorithms, cs.DS,Computer Science, Learning, cs.LG,Mathematics, Statistics, math.ST,Statistics, Machine Learning, stat.ML,Statistics, Statistics Theory, stat.TH
Abstract:
We study the problem of testing discrete distributions with a focus on the
high probability regime. Specifically, given samples from one or more discrete
distributions, a property $\mathcal{P}$, and parameters $0< \epsilon, \delta
<1$, we want to distinguish {\em with probability at least $1-\delta$} whether
these distributions satisfy $\mathcal{P}$ or are $\epsilon$-far from
$\mathcal{P}$ in total variation distance. Most prior work in distribution
testing studied the constant confidence case (corresponding to $\delta =
\Omega(1)$), and provided sample-optimal testers for a range of properties.
While one can always boost the confidence probability of any such tester by
black-box amplification, this generic boosting method typically leads to
sub-optimal sample bounds.
Here we study the following broad question: For a given property
$\mathcal{P}$, can we {\em characterize} the sample complexity of testing
$\mathcal{P}$ as a function of all relevant problem parameters, including the
error probability $\delta$? Prior to this work, uniformity testing was the only
statistical task whose sample complexity had been characterized in this
setting. As our main results, we provide the first algorithms for closeness and
independence testing that are sample-optimal, within constant factors, as a
function of all relevant parameters. We also show matching
information-theoretic lower bounds on the sample complexity of these problems.
Our techniques naturally extend to give optimal testers for related problems.
To illustrate the generality of our methods, we give optimal algorithms for
testing collections of distributions and testing closeness with unequal sized
samples.
