The pressure function for infinite equilibrium measures

Assume that $(X,f)$ is a dynamical system and $\phi:X \to [-\infty, \infty)$ is a potential such that the $f$-invariant measure $\mu_\phi$ equivalent to $\phi$-conformal measure is infinite, but that there is an inducing scheme $F = f^\tau$ with a finite measure $\mu_{\bar\phi}$ and polynomial tails $\mu_{\bar\phi}(\tau \geq n) = O(n^{-\beta})$, $\beta \in (0,1)$. We give conditions under which the pressure of $f$ for a perturbed potential $\phi+s\psi$ relates to the pressure of the induced system as $P(\phi+s\psi) = (C P(\overline{\phi+s\psi}))^{1/\beta} (1+o(1))$, together with estimates for the $o(1)$-error term. This extends results from Sarig to the setting of infinite equilibrium states. We give several examples of such systems, thus improving on the results of Lopes for the Pomeau-Manneville map with potential $\phi_t = - t\log f'$, as well as on the results by Bruin & Todd on countably piecewise linear unimodal Fibonacci maps. In addition, limit properties of the family of measures $\mu_{\phi+s\psi}$ as $s\to 0$ are studied and statistical properties (correlation coefficients and arcsine laws) under the limit measure are derived.Comment: Corrections in Section 8.2. Other minor modifications in the presentatio

Reply to Murrell et al.: Noise matters

The concept of statistical “equitability” plays a central role in the 2011 paper by Reshef et al. (1). Formalizing equitability first requires formalizing the notion of a “noisy functional relationship,” that is, a relationship between two real variables, X and Y, having the form Y=f(X)+η, where f is a function and η is a noise term. Whether a dependence measure satisfies equitability strongly depends on what mathematical properties the noise term η is allowed to have: the narrower one’s definition of noise, the weaker the equitability criterion becomes

Sample size and positive false discovery rate control for multiple testing

The positive false discovery rate (pFDR) is a useful overall measure of errors for multiple hypothesis testing, especially when the underlying goal is to attain one or more discoveries. Control of pFDR critically depends on how much evidence is available from data to distinguish between false and true nulls. Oftentimes, as many aspects of the data distributions are unknown, one may not be able to obtain strong enough evidence from the data for pFDR control. This raises the question as to how much data are needed to attain a target pFDR level. We study the asymptotics of the minimum number of observations per null for the pFDR control associated with multiple Studentized tests and $F$ tests, especially when the differences between false nulls and true nulls are small. For Studentized tests, we consider tests on shifts or other parameters associated with normal and general distributions. For $F$ tests, we also take into account the effect of the number of covariates in linear regression. The results show that in determining the minimum sample size per null for pFDR control, higher order statistical properties of data are important, and the number of covariates is important in tests to detect regression effects.Comment: Published at http://dx.doi.org/10.1214/07-EJS045 in the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Confidence bands for a log-concave density

We present a new approach for inference about a log-concave distribution: Instead of using the method of maximum likelihood, we propose to incorporate the log-concavity constraint in an appropriate nonparametric confidence set for the cdf $F$. This approach has the advantage that it automatically provides a measure of statistical uncertainty and it thus overcomes a marked limitation of the maximum likelihood estimate. In particular, we show how to construct confidence bands for the density that have a finite sample guaranteed confidence level. The nonparametric confidence set for $F$ which we introduce here has attractive computational and statistical properties: It allows to bring modern tools from optimization to bear on this problem via difference of convex programming, and it results in optimal statistical inference. We show that the width of the resulting confidence bands converges at nearly the parametric $n^{-\frac{1}{2}}$ rate when the log density is $k$-affine.Comment: Added more experiments, other minor change